Reliability allocation method, device, computer equipment and storage medium

Abstract

The present application relates to a reliability allocation method, apparatus, computer device, storage medium, and computer program product. The method comprises: determining a target weight vector based on an interval relationship matrix, wherein the interval relationship matrix is a matrix obtained from a priority relationship matrix, and the priority relationship matrix is a matrix determined based on a scoring result obtained by scoring constraints that affect reliability allocation; obtaining a first allocation index for the reliability of equipment in an equipment system; and determining a second allocation index for the reliability of a primary system in the equipment system based on a first relationship formula, wherein the first relationship formula is a relationship formula between the target weight vector, the allocation index, and the second allocation index. This method can improve the accuracy of reliability allocation results.

Description

Technical Field

[0001] The present application relates to the technical field of reliability allocation, and in particular to a reliability allocation method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] Reliability allocation is a crucial foundational task during equipment development, crucial for the determination and quantitative analysis of reliability indicators. Reliability allocation involves taking the system reliability indicators specified during the equipment development phase and, according to specific allocation principles and methods, breaking them down from top to bottom to rationally allocate them to various systems, subsystems, and equipment.

[0003] In the process of modern equipment development, the characteristics of system complexity and functional diversification are becoming more and more obvious. There are many factors that affect the reliability of complex systems. Moreover, the factors affecting reliability include some uncertain factors, and some uncertain factors cannot be described quantitatively. Therefore, there is a lack of relevant reliability data, which is not conducive to the development of allocation work.

[0004] Currently, the scoring allocation method is mainly used to allocate reliability for complex systems. However, this method relies heavily on the experience and level of the scoring experts, and the allocation results are highly subjective, resulting in low accuracy of the allocation results. Summary of the Invention

[0005] Based on this, it is necessary to provide a reliability allocation method, apparatus, computer device, computer-readable storage medium and computer program product that can improve the accuracy of reliability allocation results in response to the above technical problems.

[0006] In a first aspect, the present application provides a reliability allocation method. The method comprises:

[0007] Determining a target weight vector based on an interval relationship matrix, wherein the interval relationship matrix is a matrix obtained according to a priority relationship matrix, and the priority relationship matrix is a matrix determined according to a scoring result obtained by scoring the constraint conditions affecting the reliability distribution;

[0008] Obtaining a first distribution indicator of reliability of equipment in the equipment system;

[0009] According to a first relational expression, a second allocation index of the reliability of the first-level system in the equipment system is determined, wherein the first relational expression is a relational expression among the target weight vector, the first allocation index, and the second allocation index.

[0010] In one embodiment, the method further comprises:

[0011] Obtaining the reliability relationship between the primary system and each subsystem of the primary system;

[0012] According to the target weight vector, determining the minimum element value among the element values in the target weight vector;

[0013] Determining a third allocation 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 relational expression between the reliability relationship, the second allocation index, a ratio of each element value in the target weight vector to the minimum element value, and the third allocation index;

[0014] According to the third relationship, the fourth distribution index of the reliability of each subsystem in other subsystems is determined, wherein the third relationship is the relationship between the third distribution index, the fourth distribution index, and the ratio of the element value corresponding to the subsystem to the minimum element value, and the other subsystems include the systems in the subsystems except the subsystem corresponding to the minimum element value.

[0015] In one embodiment, determining the target weight vector based on the interval relationship matrix includes:

[0016] Determine a weight vector of a fuzzy consistency matrix according to a fourth relationship, wherein the fourth relationship is a relationship between a reciprocal judgment matrix, the weight vector, and a most recently determined historical weight vector of the fuzzy consistency matrix, wherein the reciprocal judgment matrix is a matrix obtained by transforming the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined based on a matrix obtained by randomly sampling the interval relationship matrix;

[0017] If the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold, the weight vector is used as the target weight vector.

[0018] In one embodiment, the method further comprises:

[0019] 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 relationship is repeated until the weight vector corresponding to the difference less than or equal to the first preset difference threshold is determined, and the weight vector corresponding to the difference less than or equal to the first preset difference threshold is used as the target weight vector.

[0020] In one embodiment, determining the target weight vector based on the interval relationship matrix further includes:

[0021] Determine a weighted weight vector of weight vectors corresponding to M differences that are less than or equal to the first preset difference threshold, where M is an integer greater than 1;

[0022] Determining a weight vector difference between the weighted weight vector and a historical weighted weight vector, wherein the historical weighted weight vector is a weight vector of the most recently determined N weight vectors, the N weight vectors being the front weight vectors among the M weight vectors corresponding to differences that are less than or equal to the first preset difference threshold, and N is equal to the difference between M and 1;

[0023] If the weight vector difference is less than or equal to a second preset difference threshold, the weighted weight vector is used as the target weight vector.

[0024] In one embodiment, the method further comprises:

[0025] If the weight vector difference is greater than the second preset difference threshold, determining a weighted weight vector of the weight vectors corresponding to M+1 differences that are less than or equal to the first preset difference threshold;

[0026] Taking M+1 as M, and repeatedly performing the step of determining the weight vector difference between the weighted weight vector and the historical weighted weight vector until a weighted weight vector corresponding to a weight vector difference that is less than or equal to the second preset difference threshold is determined;

[0027] The weighted weight vector corresponding to the determined weight vector difference that is less than or equal to the second preset difference threshold is used as the target weight vector.

[0028] In one embodiment, the method further comprises:

[0029] Obtaining multiple scoring results, each of which is a result obtained by scoring multiple constraint conditions of the equipment system;

[0030] According to each scoring result, determine the corresponding priority relationship matrix;

[0031] The interval relationship matrix is determined according to the priority relationship matrix corresponding to each scoring result.

[0032] In a second aspect, the present application further provides a reliability allocation device. The device comprises:

[0033] A first determination module is configured to determine a target weight vector based on an interval relationship matrix, wherein the interval relationship matrix is a matrix obtained according to a priority relationship matrix, and the priority relationship matrix is a matrix determined according to a scoring result obtained by scoring constraint conditions affecting reliability allocation;

[0034] A first acquisition module, configured to acquire a first distribution indicator of reliability of equipment in the equipment system;

[0035] The second determination module is used to determine a second allocation index of the reliability of the first-level system in the equipment system according to a first relationship, wherein the first relationship is a relationship between the target weight vector, the first allocation index and the second allocation index.

[0036] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0038] In a fifth aspect, the present application further provides a computer program product, comprising a computer program that implements the steps of any of the above methods when executed by a processor.

[0039] The reliability allocation method, apparatus, computer device, storage medium, and computer program product described above determine a target weight vector based on an interval relationship matrix, where the interval relationship matrix is derived from a priority relationship matrix, which is determined based on the scoring results of the constraints affecting reliability allocation. This determines a first reliability allocation index for the equipment in the equipment system, and then determines a second reliability allocation index for the primary system in the equipment system based on a first relationship equation, where the first relationship equation is the relationship between the target weight vector, the first allocation index, and the second allocation index. Traditional scoring allocation methods directly use the expert scoring results of the constraints as input parameters for weighted analysis, conduct a weighted analysis based on a weighted analysis method, analyze and calculate specific weights for the objects to be allocated, and then allocate reliability based on the specific weights. Because the evaluation of constraints in traditional scoring allocation methods is significantly influenced by human subjectivity during the weighted analysis process, the evaluation may be unreasonable and inconsistent with reality, resulting in deviations in the final allocation results. The reliability allocation method provided in this embodiment determines a priority relationship matrix based on the expert's scoring results for the constraints, and then derives an interval relationship matrix based on the priority relationship matrix. This then determines a target weight vector based on the interval relationship matrix, and then performs reliability allocation based on the target weight vector. This solves the problem of high subjectivity in allocation results caused by directly weighting the constraint scoring results in traditional methods, and further addresses the low precision of allocation results in traditional methods, improving the accuracy of reliability allocation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1A schematic diagram of the overall process of a reliability allocation method provided in an embodiment of the present application;

[0041] Figure 2 A flow chart of a reliability allocation method provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a process for determining the reliability index of each subsystem provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of a process for determining a target weight vector through a single simulation provided in an embodiment of the present application;

[0044] Figure 5 A schematic diagram of a process for determining a target weight vector through multiple simulations provided in an embodiment of the present application;

[0045] Figure 6 This is another flowchart of determining a target weight vector through multiple simulations provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of a process for determining an interval relationship matrix provided in an embodiment of the present application;

[0047] Figure 8 This is a schematic structural diagram of a reliability allocation device provided in an embodiment of the present application;

[0048] Figure 9 This is a diagram of the internal structure of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0050] In this embodiment, a reliability allocation method is provided. This embodiment uses the method applied to a computer device as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a computer device and a server, and is implemented through the interaction between the computer device and the server.

[0051] In the development of modern equipment, the complexity and functional diversity of equipment systems are becoming increasingly evident, placing higher demands on the reliability of these systems. Reliability allocation, as a key reliability task, is crucial for the reliability design and analysis of equipment systems. However, due to the lack of reliability data and ambiguous information during the development phase of complex equipment, traditional single reliability allocation methods are limited and cannot be fully applied to the reliability allocation of modern complex equipment systems. This makes it difficult to allocate reliability indicators for complex equipment systems, hindering the in-depth development of equipment reliability design and analysis.

[0052] To address the above issues, this embodiment proposes a reliability allocation method that utilizes Monte Carlo simulation combined with fuzzy hierarchical analysis. This method can be applied to large, complex equipment systems with numerous components, complex structural relationships, and diverse mission functions, such as ships, satellites, and radars. The reliability allocation method provided in this embodiment effectively addresses the issue of allocating reliability indicators for primary systems and subsystems in large, complex systems such as ships, satellites, and radars. This method plays a significant role in promoting the engineering application of simulation-based reliability allocation technology in large, complex systems, advancing equipment reliability engineering, and improving the overall reliability level of equipment.

[0053] In order to more clearly introduce the reliability allocation method provided in this embodiment, Figure 1 Explain. Figure 1 , Figure 1 A schematic diagram of the overall process of a reliability allocation method provided in an embodiment of the present application.

[0054] Equipment systems have a hierarchical relationship, from high to low, including equipment, first-level systems, subsystems, devices, etc. Reliability allocation is a process from high to low levels. First, based on the reliability indicators of the equipment in the entire equipment system, indicators are allocated to each first-level system; based on the first-level system allocation results, indicators are allocated to each subsystem under the first-level system in accordance with the same technical solution.

[0055] like Figure 1As shown, the reliability allocation method provided in this embodiment first requires a system definition of the equipment system. Fuzzy hierarchical analysis is then performed based on the system definition. The purpose of the fuzzy hierarchical analysis is to determine the interval relationship matrix. Simulation and solution are then performed using the Monte Carlo simulation method based on the fuzzy hierarchical analysis. The simulation and solution process includes: simulation sampling and initial weight vector solution, accuracy determination under a single simulation, and accuracy determination under multiple simulations, and ultimately determining the target weight vector. Based on the ultimately determined target weight vector, reliability allocation can be performed on the equipment system. Reliability allocation for the equipment system includes performing reliability allocation on the primary system within the equipment system, and then, based on the reliability allocation results for the primary system, performing reliability allocation on the subsystems of the primary system within the equipment system, thereby obtaining the reliability allocation results.

[0056] System definition involves analyzing and defining the equipment system for which reliability allocation is to be performed. This includes analyzing and defining the equipment type, system composition, functional structure relationships, and constraints. Constraints include the complexity, importance, technical level, environmental conditions, and operating hours of each unit, factors that influence reliability allocation for the equipment system. Furthermore, by clarifying the functional structure of the system and the logical relationships between the components of the equipment system, a reliability model for the equipment system is established.

[0057] Figure 2 Schematic diagram of the reliability allocation method provided in the embodiment of the present application, the method is applied to a computer device or a server. In one embodiment, Figure 2 As shown, the following steps are included:

[0058] S201 , determining a target weight vector based on an interval relationship matrix, wherein the interval relationship matrix is a matrix obtained according to a priority relationship matrix, and the priority relationship matrix is a matrix determined according to a scoring result obtained by scoring constraint conditions affecting reliability allocation.

[0059] In this embodiment, the priority relationship matrix is determined based on the scoring results obtained by experts on the constraints affecting reliability allocation. The interval relationship matrix is then determined based on the priority relationship matrix, and the target weight vector can be determined through the interval relationship matrix. The interval relationship matrix is represented as P, and the target weight vector is represented as W. F , W F =(ω1,ω2,……,ω n ) T .

[0060] in, When the second allocation index of the reliability of the first-level system needs to be calculated based on the first allocation index of the reliability of the equipment, n is the number of the next-level systems of the equipment.

[0061] Based on the interval relationship matrix, the target weight vector is determined. The target weight vector can be determined by accuracy judgment under a single simulation, or by accuracy judgment under multiple simulations. This embodiment does not impose any restrictions on this.

[0062] S202: Obtain a first distribution indicator of reliability of equipment in the equipment system.

[0063] In this embodiment, assuming that the allocation index of the equipment in the equipment system is reliability, the first allocation index of the reliability of the equipment in the equipment system, that is, the reliability allocation index of the equipment, is expressed as R m , which is known. It is understandable that the allocation index can also be set to other contents. This embodiment is described with the allocation index being reliability.

[0064] S203 : Determine a second allocation index of reliability of a first-level system in the equipment system according to a first relationship expression, wherein the first relationship expression is a relationship expression between the target weight vector, the first allocation index, and the second allocation index.

[0065] In this embodiment, the second allocation index of each first-level system in the equipment system, that is, the reliability allocation index of the first-level system is assumed to be R mi Since the first-level system is generally a series model, the first relation (1) is valid. The first relation is the target weight vector, the first allocation index R m and the second allocation index R mi The relationship between .

[0066]

[0067] Here, i is 1-n, and n is the number of primary systems.

[0068] In the first relation, R m It is known that the target weight vector is known, that is, each element ω in the target weight vector i Therefore, according to the first relationship, the second allocation index R of each primary system can be obtained. mi For example, if n=4 and four primary systems are connected in series, the second allocation index of the first primary system in the series model is Second allocation index of the second level system Second allocation index of the third level system Second allocation index of the 4th level system

[0069] The reliability allocation method provided in this embodiment determines a target weight vector based on an interval relationship matrix. The interval relationship matrix is derived from a priority relationship matrix, which is determined based on the scoring results of the constraints affecting reliability allocation. The method then obtains a first reliability allocation index for the equipment in the equipment system and determines a second reliability allocation index for the first-level system in the equipment system based on a first relationship. The first relationship is the relationship between the target weight vector, the first allocation index, and the second allocation index. Traditional scoring allocation methods directly use the expert scoring results of the constraints as input parameters for weighted analysis. Based on a weighted analysis method, a weighted analysis is performed to calculate specific weights for the objects to be allocated, and reliability allocation is then performed based on these specific weights. Because the evaluation of constraints in traditional scoring allocation methods is significantly influenced by human subjectivity during the weighted analysis process, there may be cases where the evaluation is unreasonable or inconsistent with reality, leading to deviations in the final allocation results. The reliability allocation method provided in this embodiment, however, determines a priority relationship matrix based on the expert scoring results of the constraints and, based on the priority relationship matrix, obtains an interval relationship matrix. The target weight vector is then determined based on the interval relationship matrix, and reliability allocation is then performed based on the target weight vector. Therefore, the problem of high subjectivity of allocation results caused by directly weighted analysis of the scoring results of the constraint conditions in the traditional method is solved, and the problem of low precision of allocation results in the traditional method is solved, thereby improving the accuracy of the reliability allocation results.

[0070] Figure 3 This is a flow chart of determining the reliability index of each subsystem provided in the embodiment of the present application, with reference to Figure 3 This embodiment relates to an implementation method of how to determine the reliability index of each subsystem. Based on the above embodiment, the above reliability allocation method further includes the following steps:

[0071] S301, obtaining the reliability relationship between the primary system and each subsystem of the primary system.

[0072] In this embodiment, the reliability relationship between the primary system and its subsystems is assumed to be R, where R is a function expression that can be set according to the specific circumstances of the equipment system and is not limited in this embodiment.

[0073] S302: Determine the minimum element value among the element values in the target weight vector according to the target weight vector.

[0074] In this embodiment, the target weight vector is represented by W F =(ω1,ω2,……,ω n ) T, n is the number of lower levels. At this time, the third and fourth allocation indices of the reliability of each subsystem need to be calculated based on the second allocation indices of the reliability of the primary system, so n is the number of subsystems.

[0075] ω i are the elements in the target weight vector. That is, when i is 1 to n, ω1 to ω n W F Each element in . Let ω i The minimum value in is ω0, that is, ω0 is equal to ω1~ω n Therefore, ω0 is the minimum value of each element in the target weight vector.

[0076] S303. Determine a third allocation index of the reliability of the subsystem corresponding to the minimum element value based on the second relationship, wherein the second relationship is a relationship between the reliability relationship, the second allocation index, the ratio of each element value to the minimum element value in the target weight vector, and the third allocation index.

[0077] In this embodiment, the reliability allocation method is used to calculate the allocation index of each subsystem of the first-level system according to the allocation index of the first-level system. Suppose the reliability allocation index of a first-level system is R s , R s is a known value.

[0078] The above S203 has calculated the second allocation index of each first-level system in the equipment system, that is, the reliability allocation index R of the first-level system. mi , so this embodiment believes that R s =R mi The third allocation index of the subsystem corresponding to the minimum element ω0 is R0, that is, the reliability allocation value of the minimum subsystem corresponding to ω0 is R0. Therefore, the reliability allocation value of the subsystem with the smallest weight vector can be used to represent the reliability allocation values of other subsystems, and it is an unknown number.

[0079] According to the reliability model of the first-level system, the following equation (2) holds true.

[0080] R s (R1, R2, ..., R n )=R s (2)

[0081] Therefore, the second relation (3) is established, and the second relation is the reliability relation R and the second allocation index R s And the value of each element in the target weight vector ω i The ratio to the minimum element value ω0 And the relationship between the third allocation index R0.

[0082]

[0083] In the second relation, R is known, R s Known, equal to R mi , the target weight vector is known, that is, ω in the target weight vector i The smallest element ω0 is also known. Therefore, only R0 is unknown in the second relation. The reliability allocation problem of the primary system can be transformed into a problem of solving R0. Based on the second relation, the third allocation index R0 can be calculated.

[0084] S304. Determine the fourth distribution index of the reliability of each subsystem in other subsystems based on the third relationship, wherein the third relationship is the relationship between the third distribution index, the fourth distribution index, and the ratio of the element value corresponding to the subsystem to the minimum element value, and the other subsystems include the systems in each subsystem except the subsystem corresponding to the minimum element value.

[0085] In this embodiment, it is assumed that the fourth distribution index of the reliability of each subsystem, that is, the reliability distribution value of each subsystem is expressed as R i At the same time, equation (4) representing the third relation holds.

[0086]

[0087] Therefore, after calculating the third allocation index R0, the fourth allocation index R0 based on the reliability of each subsystem can be calculated. i .

[0088] For example, if n=5, the first-level system corresponds to 5 subsystems, and the reliability distribution value of the first subsystem is Reliability distribution value of the second subsystem Reliability distribution value of the third subsystem Reliability distribution value of the 4th subsystem Reliability distribution value of the 5th subsystem It can be understood that, since the reliability distribution value of the minimum subsystem corresponding to ω0 is R0, if the minimum value among R1 to R5 is R3, then R0=R3.

[0089] This embodiment obtains the reliability relationship between the primary system and its subsystems, and determines the minimum element value among the elements in the target weight vector based on the target weight vector. Furthermore, based on the second relationship, the third reliability distribution index for the subsystem corresponding to the minimum element value is determined. Based on the third relationship, the fourth reliability distribution index for each subsystem in the other subsystems is determined. Because the third and fourth reliability distribution indexes for each subsystem are determined based on the target weight vector determined based on the interval relationship moment, the accuracy of the reliability distribution results is improved.

[0090] Figure 4 This is a flow chart of determining a target weight vector by a single simulation provided in an embodiment of the present application, with reference to Figure 4 This embodiment relates to an implementation method of how to determine the target weight vector through a single simulation. Based on the above embodiment, the above S201 further includes the following steps:

[0091] S401, determine the weight vector of the fuzzy consistency matrix according to the fourth relationship, wherein the fourth relationship is the relationship between the reciprocal judgment matrix, the weight vector and the historical weight vector of the fuzzy consistency matrix determined most recently, wherein the reciprocal judgment matrix is a matrix obtained by transforming the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined based on a matrix obtained by randomly sampling the interval relationship matrix.

[0092] In this embodiment, the interval relationship matrix P is randomly and uniformly sampled to obtain the matrix after sampling. Based on the sampled matrix A fuzzy consistency matrix can be established

[0093]

[0094] where r ij It can be determined by the following formulas (5) to (7).

[0095]

[0096]

[0097]

[0098] Get the fuzzy consistency matrix Then, the fuzzy consistency matrix is solved by row sum normalization method The initial weight vector is denoted as W0, and the following formula (8) can be obtained.

[0099]

[0100] In order to solve the accuracy and convergence problem of the solution in a single simulation, this embodiment uses the following power method to calculate the weight vector with higher accuracy. Transformed into a reciprocal judgment matrix

[0101] Among them, e ij It can be determined by the following formula (9).

[0102]

[0103] Taking W0 as the initial weight vector, according to the following formula (10), i.e. the fourth relational formula, an iterative operation can be performed to determine the weight vector of the fuzzy consistency matrix. The fourth relational formula is the reciprocal judgment matrix Weight vector W m+1 And the historical weight vector W of the most recently determined fuzzy consistency matrix m The relationship between .

[0104]

[0105] Where m is an integer greater than or equal to 0, and m+1 represents the number of iterations. For example, after obtaining the initial weight vector W0, m is 0. At this time, the weight vector W1 of the fuzzy consistency matrix of the first iteration can be calculated according to the fourth relationship.

[0106] S402: If the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold, the weight vector is used as a target weight vector.

[0107] In this embodiment, by giving a first preset difference threshold ε1, for any ε1>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, when the following formula (11) is established, it means that the accuracy under a single simulation has met the requirements and the iteration can be terminated.

[0108] ‖W m+1 ‖ max -‖W m ‖ max ≤ε1 (11)

[0109] At the end of the iteration, the weight vector obtained is recorded as W k , at this time k=m+1, W k Satisfies formula (12).

[0110]

[0111] For example, if m is 0, the first iteration weight vector W1 can be calculated according to the fourth relationship. If ‖W1‖ max -‖W0‖ max≤ε1, then k=m+1=1, and W k That is, W1 is the target weight vector,

[0112] In this embodiment, the weight vector of the fuzzy consistency matrix is determined according to the fourth relationship, wherein the fourth relationship is the relationship between the reciprocal judgment matrix, the weight vector, and the historical weight vector of the fuzzy consistency matrix determined most recently, wherein the reciprocal judgment matrix is a matrix obtained by transforming the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined based on a matrix obtained by randomly sampling the interval relationship matrix. If the difference between the weight vector and the historical weight vector is less than or equal to the first preset difference threshold, the weight vector is used as the target weight vector. Since the fourth relationship is iterated, a weight vector under a single simulation that meets the accuracy requirements can be obtained, and thus this weight vector is used as the target weight vector, and reliability allocation is performed using the target weight vector, thereby improving the accuracy of the reliability allocation result.

[0113] Optionally, the above S402 may be implemented in the following manner:

[0114] 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 relationship is repeated until the weight vector corresponding to the difference less than or equal to the first preset difference threshold is determined, and the weight vector corresponding to the difference less than or equal to the first preset difference threshold is used as the target weight vector.

[0115] In this embodiment, if ‖W m+1 ‖ max -‖W m ‖ max >ε1, then m continues to increase by 1, and iterate until a solution that satisfies ‖W is found. m+1 ‖ max -‖W m ‖ max ≤ε1, and W at this time k =W m+1 Target weight vector.

[0116] For example, if m is 0, the first iteration weight vector W1 can be calculated according to the fourth relationship. If ‖W1‖ max -‖W0‖ max >ε1, then continue the iteration. At this time, m=1, and the weight vector W2 of the second iteration can be calculated according to the fourth relationship. If ‖W2‖ max -‖W1‖ max >ε1, then k=m+1=2, and W k That is, W2 is used as the target weight vector.

[0117] In this embodiment, if the difference between the weight vector and the historical weight vector is greater than a first preset difference threshold, the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relationship is repeated until a weight vector corresponding to a difference less than or equal to the first preset difference threshold is determined. The weight vector corresponding to the determined difference less than or equal to the first preset difference threshold is used as the target weight vector. By continuing to iterate the weight vector under a single simulation that does not meet the accuracy requirements, a weight vector under a single simulation that meets the accuracy requirements is obtained, and this weight vector is used as the target weight vector. Reliability allocation is then performed using the target weight vector, thereby improving the accuracy of the reliability allocation results.

[0118] Figure 5 This is a flow chart of determining a target weight vector by multiple simulations provided in an embodiment of the present application, with reference to Figure 5 This embodiment relates to an implementation method of how to determine the target weight vector through multiple simulations. Based on the above embodiment, the above S201 further includes the following steps:

[0119] S501: Determine a weighted weight vector of weight vectors corresponding to M differences that are less than or equal to a first preset difference threshold, where M is an integer greater than 1.

[0120] In this embodiment, multiple simulations are performed, and the sorting weight vector obtained by M single simulations is recorded as Q M , Q M The weight vector Q is the weighted weight vector of the weight vectors corresponding to the M differences that are less than or equal to the first preset difference threshold. M =(ω 1M ,ω 2M ,……,ω nM ) T , M is the number of single simulations that meet the accuracy requirements, that is, M single simulations get M sorting weight vectors, n is the number of lower levels. Then, the weighted average method is used to calculate the weighted weight vector of the weight vectors obtained by M single simulations that meet the accuracy requirements, and it is recorded as W M ,

[0121] Among them, each element is The following formula (13) is satisfied, and j ranges from 1 to n.

[0122]

[0123] For example, if n=4 and M is 2, then a total of 2 single simulations that meet the accuracy requirements are performed. Assuming that the first simulation starts from the random sampling step and iterates only 2 times, the weight vector W2 that meets the accuracy requirements is obtained; the second simulation starts from the random sampling step and iterates 5 times, then the weight vector W5 that meets the accuracy requirements is obtained. Therefore, the sorting weight vector obtained by these two single simulations is recorded as QM , M takes 1 and 2.

[0124] Q1=(ω 11 ,ω 21 ,ω 31 ,ω 41 ) T =W2

[0125] Q2=(ω 12 ,ω 22 ,ω 31 ,ω 42 ) T =W5

[0126] Calculate the weighted weight vector for Q1 and Q2, and get the weighted weight vector W M , that is, W 2 ,

[0127] Among them, W 2 The elements in can be determined according to equation (13).

[0128]

[0129]

[0130]

[0131]

[0132] where ω 11 、ω 21 、ω 31 and ω 41 According to the sorting weight vector Q1, namely W2, ω 12 、ω 22 、ω 32 and ω 42 It can be known based on the sorting weight vector Q2, namely W5.

[0133] S502, determine the weight vector difference between the weighted weight vector and the historical weighted weight vector, wherein the historical weighted weight vector is the weighted weight vector of the N weight vectors determined most recently, the N weight vectors are the front weight vectors among the weight vectors corresponding to the M differences that are less than or equal to the first preset difference threshold, and N is equal to the difference between M and 1.

[0134] In this embodiment, the weighted vector W is determined M and the historical weighted weight vector W M-1 The weight vector difference is ‖W M -W M-1 For example, n=4, M is 3, and the weighted vector is W3 , W 3 It is calculated by sorting weight vectors Q1, Q2 and Q3. The historical weight vector is W 2 , W 2 It is calculated based on the front weight vectors Q1 and Q2. More specifically, according to According to formula (13), Where Q1=(ω 11 ,ω 21 ,ω 31 ,ω 41 ) T , Q2=(ω 12 ,ω 22 ,ω 32 ,ω 42 ) T .

[0135] S503: If the weight vector difference is less than or equal to a second preset difference threshold, the weighted weight vector is used as a target weight vector.

[0136] In this embodiment, by giving a second preset difference threshold ε2, for any ε2>0, the weight vector difference between the weighted weight vector and the historical weighted weight vector is determined, and if the weight vector difference is less than or equal to the second preset difference threshold, that is, when the following formula (14) holds, the iteration can be terminated.

[0137] ‖W M -W M-1 ‖≤ε2 (14)

[0138] When the iteration ends, the weighted weight vector is obtained as the target weight vector.

[0139] For example, if M is 2, calculate ‖W 2 -W 1 ‖≤ε2, then the weighted weight vector W 2 as the target weight vector.

[0140] This embodiment determines the weighted weight vector of the weight vectors corresponding to M differences less than or equal to a first preset difference threshold, where M is an integer greater than 1, and then determines the weight vector difference between the weighted weight vector and the historical weighted weight vector, where the historical weighted weight vector is the weighted weight vector of the N weight vectors determined most recently, and the N weight vectors are the front weight vectors among the weight vectors corresponding to M differences less than or equal to the first preset difference threshold, where 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 used as the target weight vector. Due to the judgment of the second preset difference threshold, the weighted weight vector under multiple simulations that meets the accuracy requirement can be obtained based on the weight vector obtained by a single simulation that meets the concise requirement, and thus this weighted weight vector is used as the target weight vector, and reliability allocation is performed based on the target weight vector, thereby improving the accuracy of the reliability allocation result.

[0141] Figure 6 This is another flow chart of determining the target weight vector by multiple simulations provided in the embodiment of the present application, with reference to Figure 6 This embodiment relates to an implementation method of how to determine the target weight vector. Based on the above embodiment, the above reliability allocation method further includes the following steps:

[0142] S601: If the weight vector difference is greater than the second preset difference threshold, determine a weighted weight vector of M+1 weight vectors corresponding to differences that are less than or equal to the first preset difference threshold.

[0143] In this embodiment, if the weight vector difference is greater than the second preset difference threshold, ie, ‖W M -W M-1 ‖>ε2, then M+1 is used to determine the weighted weight vectors corresponding to the M+1 differences that are less than or equal to the first preset difference threshold. For example, when M is 2, the weighted weight vector W corresponding to Q1 and Q2 is calculated. 2 , ‖W 2 -W 1 ‖>ε2, then M+1, and then start from the random sampling step again, iterate, and get the weight vector W that meets the single simulation accuracy requirement k =Q3, then the corresponding weighted weight vector W is obtained based on Q1, Q2 and Q3 3 .

[0144] S602: Set M+1 as M and repeatedly perform the step of determining the weight vector difference between the weighted weight vector and the historical weighted weight vector until a weighted weight vector corresponding to a weight vector difference that is less than or equal to a second preset difference threshold is determined.

[0145] In this embodiment, according to step S601, for example, the weighted weight vector W is obtained. 3. In this case, M+1 is taken as M, M=3. If ‖W 3 -W 2 ‖≤ε2, the calculation can be stopped. But if ‖W 3 -W 2 ‖>ε2, then M continues to increase by 1, and the weighted weight vector W corresponding to Q1, Q2, Q3 and Q4 is calculated 4 , and make a judgment, if ‖W 4 -W 3 ‖>ε2, the calculation can be stopped.

[0146] S603: Use the weighted weight vector corresponding to the determined weight vector difference that is less than or equal to the second preset difference threshold as the target weight vector.

[0147] In this embodiment, the weight vector difference W that satisfies formula (14), that is, is less than or equal to the second preset difference threshold, is finally determined. M The corresponding weighted weight vector is used as the target weight vector. For example, the W calculated in S602 4 as the target weight vector.

[0148] In this embodiment, if the weight vector difference is greater than the second preset difference threshold, the weight vector corresponding to the weight vectors with M+1 differences less than or equal to the first preset difference threshold is determined, and M+1 is used as M. The step of determining the weight vector difference between the weight vector and the historical weight vector is repeatedly performed until the weight vector corresponding to the weight vector difference less than or equal to the second preset difference threshold is determined, and then the weight vector corresponding to the weight vector difference less than or equal to the second preset difference threshold is used as the target weight vector. Since the weight vectors under multiple simulations that do not meet the accuracy requirement are continuously calculated, the weight vectors under multiple simulations that meet the accuracy requirement are obtained based on the weight vectors obtained from a single simulation that meets the essential requirement, and this weight vector is used as the target weight vector, and reliability allocation is performed based on the target weight vector, thereby improving the accuracy of the reliability allocation result.

[0149] Figure 7 This is a flow chart of determining an interval relationship matrix provided in an embodiment of the present application, with reference to Figure 7 This embodiment relates to an implementation method of how to determine the interval relationship matrix. Based on the above embodiment, the above reliability allocation method further includes the following steps:

[0150] S701, obtaining multiple scoring results, each scoring result is a result obtained by scoring multiple constraint conditions of the equipment system.

[0151] In this embodiment, the construction of the priority relationship matrix relies on expert ratings of various constraints, such as complexity and technical maturity. Therefore, to reduce or eliminate the influence of human subjective factors, this embodiment simultaneously collects scores from multiple experts on multiple constraints of the equipment system, resulting in multiple rating results.

[0152] S702: Determine a corresponding priority relationship matrix based on each scoring result.

[0153] In this embodiment, after obtaining each scoring result, the corresponding priority relationship matrix is determined after comprehensively considering the expert's scoring results for multiple constraints.

[0154] More specifically, after an expert scores multiple constraints, a priority relationship matrix is obtained after comprehensive consideration of the scoring results, which is recorded as a ij This is the comparative score of the constraints of subsystem i relative to subsystem j, using a scale of 0.1-0.9, where n is the number of subsystems. 0.5 indicates that the two subsystems have the same score weight; 0.5-0.9 indicates that the weight of subsystem i relative to subsystem j increases, and 0.1-0.5 indicates that the weight of subsystem i relative to subsystem j decreases. Based on the properties of the priority relationship matrix, it generally satisfies the following equations (15) to (17).

[0155] a ij =0.5 (15)

[0156] a ij +a ji =1 (16)

[0157] 0 ij <1 (17)

[0158] After an expert scores multiple constraints, a priority relationship matrix can be determined Therefore, after multiple experts score multiple constraints, multiple corresponding priority relationship matrices can be determined.

[0159] S703: Determine an interval relationship matrix according to the priority relationship matrix corresponding to each scoring result.

[0160] In this embodiment, after multiple experts have scored multiple constraints, multiple corresponding priority relationship matrices can be determined. Each priority relationship matrix contains the score comparison value of the constraint condition of subsystem i relative to that of subsystem j, which is equivalent to establishing a score interval. ij The minimum and maximum values of can be used to determine the interval relationship matrix. The interval relationship matrix is denoted as P and is expressed as follows (18).

[0161] ​

[0162] in, Indicates the minimum value of the interval, represents the maximum value of the interval, and the following equations (19) to (20) hold.

[0163]

[0164]

[0165] According to formula (15), when i=n, due to a nn =a ii =0.5, so the interval relationship matrix P is further expressed as formula (21).

[0166]

[0167] This embodiment obtains multiple scoring results, each resulting from scoring multiple constraints of an equipment system. Based on each scoring result, a corresponding priority relationship matrix is determined. Furthermore, based on the priority relationship matrix corresponding to each scoring result, an interval relationship matrix is determined. Furthermore, based on the priority relationship matrix corresponding to each scoring result, an interval relationship matrix is determined. By using multiple experts to score multiple constraints and determining the interval relationship matrix based on the scoring intervals, the influence of human subjectivity is reduced or eliminated. This improves the accuracy of reliability allocation results.

[0168] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0169] Based on the same inventive concept, the present application also provides a reliability allocation device for implementing the reliability allocation method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more reliability allocation device embodiments provided below can be found in the limitations of the reliability allocation method described above and will not be repeated here.

[0170] Reference Figure 8 , Figure 8 800 is a schematic diagram of a reliability allocation device provided in an embodiment of the present application. The device 800 includes: a first determination module 801, a first acquisition module 802, and a second determination module 803, wherein:

[0171] The first determination module 801 is used to determine a target weight vector based on an interval relationship matrix, wherein the interval relationship matrix is a matrix obtained according to a priority relationship matrix, and the priority relationship matrix is a matrix determined according to a scoring result obtained by scoring constraint conditions affecting reliability allocation.

[0172] The first acquisition module 802 is configured to acquire a first distribution indicator of reliability of equipment in the equipment system.

[0173] The second determining module 803 is used to determine a second allocation index of the reliability of the first-level system in the equipment system according to the first relationship, wherein the first relationship is a relationship between the target weight vector, the first allocation index and the second allocation index.

[0174] The reliability allocation device provided in this embodiment determines a target weight vector based on an interval relationship matrix, where the interval relationship matrix is derived from a priority relationship matrix, which is a matrix determined based on the scoring results of the constraints affecting reliability allocation. The device then obtains a first reliability allocation index for the equipment in the equipment system and then determines a second reliability allocation index for the first-level system in the equipment system based on a first relationship. The first relationship is the relationship between the target weight vector, the first allocation index, and the second allocation index. Traditional scoring allocation methods directly use the expert scoring results of the constraints as input parameters for weighted analysis. Based on a weighted analysis method, a weighted analysis is performed to calculate the specific weights of the objects to be allocated, and reliability allocation is then performed based on these specific weights. Because the evaluation of constraints in traditional scoring allocation methods is significantly influenced by human subjectivity during the weighted analysis process, there may be cases where the evaluation is unreasonable or inconsistent with reality, resulting in deviations in the final allocation results. The reliability allocation method provided in this embodiment, however, determines a priority relationship matrix based on the expert scoring results of the constraints, and then obtains an interval relationship matrix based on the priority relationship matrix. The target weight vector is then determined based on the interval relationship matrix, and reliability allocation is then performed based on the target weight vector. Therefore, the problem of high subjectivity of allocation results caused by directly weighted analysis of the scoring results of the constraint conditions in the traditional method is solved, and the problem of low precision of allocation results in the traditional method is solved, thereby improving the accuracy of the reliability allocation results.

[0175] Optionally, the apparatus 800 further includes:

[0176] The second acquisition module is used to obtain the reliability relationship between the first-level system and each subsystem of the first-level system.

[0177] The third determining module is used to determine the minimum element value among the element values in the target weight vector according to the target weight vector.

[0178] The fourth determination module is used to determine the third allocation index of the reliability of the subsystem corresponding to the minimum element value based on the second relationship, wherein the second relationship is the relationship between the reliability relationship, the second allocation index, the ratio of each element value in the target weight vector to the minimum element value, and the third allocation index.

[0179] Optionally, the first determining module 801 includes:

[0180] The first determination unit is used to determine the weight vector of the fuzzy consistency matrix according to a fourth relationship, wherein the fourth relationship is a relationship between the reciprocal judgment matrix, the weight vector and the historical weight vector of the fuzzy consistency matrix determined most recently, wherein the reciprocal judgment matrix is a matrix obtained by transforming the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined based on a matrix obtained by randomly sampling the interval relationship matrix.

[0181] The second determining unit is configured to use the weight vector as the 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.

[0182] Optionally, the second determination unit is also used to repeat the step of determining the weight vector of the fuzzy consistency matrix according to the fourth relationship if the difference between the weight vector and the historical weight vector is greater than the first preset difference threshold, until the weight vector corresponding to the difference less than or equal to the first preset difference threshold is determined, and the weight vector corresponding to the difference less than or equal to the first preset difference threshold is used as the target weight vector.

[0183] Optionally, the first determining module 801 further includes:

[0184] The third determining unit is configured to determine a weighted weight vector of weight vectors corresponding to M differences that are less than or equal to a first preset difference threshold, where M is an integer greater than 1.

[0185] The fourth determination unit determines the weight vector difference between the weighted weight vector and the historical weighted weight vector, wherein the historical weighted weight vector is the weighted weight vector of the N weight vectors determined most recently, and the N weight vectors are the front weight vectors among the weight vectors corresponding to the M differences that are less than or equal to the first preset difference threshold, and N is equal to the difference between M and 1.

[0186] The fifth determining unit is configured to use 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.

[0187] Optionally, the fifth determining unit further includes:

[0188] The first determining subunit is configured to determine a weighted weight vector of M+1 weight vectors corresponding to differences that are less than or equal to the first preset difference threshold if the weight vector difference is greater than a second preset difference threshold.

[0189] The second determining subunit is used to take M+1 as M and repeatedly perform the step of determining the weight vector difference between the weighted weight vector and the historical weighted weight vector until a weighted weight vector corresponding to a weight vector difference less than or equal to a second preset difference threshold is determined.

[0190] The third determining subunit is configured to use the weighted weight vector corresponding to the determined weight vector difference that is less than or equal to the second preset difference threshold as the target weight vector.

[0191] Optionally, the apparatus 800 further includes:

[0192] The third acquisition module is used to obtain multiple scoring results, each scoring result is a result obtained by scoring multiple constraint conditions of the equipment system.

[0193] The second determining module is used to determine the corresponding priority relationship matrix according to each scoring result.

[0194] The third determining module is used to determine the interval relationship matrix according to the priority relationship matrix corresponding to each scoring result.

[0195] Each module in the reliability allocation device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0196] Figure 9 The internal structure diagram of the computer device in the embodiment of the present application is shown in FIG. 1 . In the embodiment, a computer device is provided, and its internal structure diagram can be as shown in FIG. Figure 9As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile 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 the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a reliability allocation method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0197] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0198] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the reliability allocation method provided in the above embodiment. The implementation principle and technical effects are similar to those of the above method embodiment and will not be repeated here.

[0199] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When executed by a processor, the computer program implements the steps of the reliability allocation method provided in the above embodiment. The implementation principle and technical effects are similar to those of the above method embodiment and will not be repeated here.

[0200] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the reliability allocation method provided in the above embodiment. The implementation principle and technical effects are similar to those of the above method embodiment and will not be repeated here.

[0201] 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 used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0202] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0203] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0204] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A reliability allocation method, characterized in that: The method comprises: Determine the target weight vector based on the interval relationship matrix; Obtaining a first distribution indicator of reliability of equipment in the equipment system; Determine a second allocation index of the reliability of the first-level system in the equipment system according to a first relationship, wherein the first relationship is a relationship between the target weight vector, the first allocation index, and the second allocation index; the first relationship includes ,in, are the elements in the target weight vector, is the first allocation indicator, is the second allocation index, i is greater than or equal to 1 and less than or equal to n, and n is the number of the next level; Obtaining a reliability relationship between the primary system and each subsystem of the primary system; Determining, according to the target weight vector, a minimum element value among the element values in the target weight vector; Determine a third allocation 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 relational expression between the reliability relationship, the second allocation index, the ratio of each element value in the target weight vector to the minimum element value, and the third allocation index; the second relational expression includes , is the third allocation index, R is the reliability relationship, is the minimum element value, ; According to the third relational expression, a fourth distribution index of the reliability of each subsystem in the other subsystems is determined, wherein the third relational expression is a relational expression between the third distribution index, the fourth distribution index, and the ratio of the element value corresponding to the subsystem to the minimum element value, and the other subsystems include the systems in the subsystems except the subsystem corresponding to the minimum element value; the third relational expression includes , allocating an index for the fourth; The method further comprises: Obtaining multiple scoring results, each of which is a result of scoring multiple constraints of the equipment system; the constraints include complexity, importance, technical level, environmental conditions, or working time; Determine a corresponding priority relationship matrix according to each of the scoring results; The interval relationship matrix is determined according to the priority relationship matrix corresponding to each of the scoring results.

2. The method according to claim 1, characterized in that The step of determining the target weight vector based on the interval relationship matrix includes: According to the fourth relationship, the weight vector of the fuzzy consistency matrix is determined, wherein the fourth relationship is a relationship between the reciprocal judgment matrix, the weight vector and the historical weight vector of the fuzzy consistency matrix determined most recently, wherein the reciprocal judgment matrix is a matrix obtained by transforming the fuzzy consistency matrix, and the fuzzy consistency matrix is a matrix determined based on a matrix obtained by randomly sampling the interval relationship matrix; the fourth relationship includes , is the reciprocal judgment matrix, is the weight vector, is the historical weight vector of the fuzzy consistency matrix determined most recently, and m+1 is the number of iterations; If the difference between the weight vector and the historical weight vector is less than or equal to a first preset difference threshold, the weight vector is used as the target weight vector.

3. The method according to claim 2, characterized in that 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 relationship is repeated until a weight vector corresponding to a difference less than or equal to the first preset difference threshold is determined, and the weight vector corresponding to the difference less than or equal to the first preset difference threshold is used as the target weight vector.

4. The method according to claim 2, characterized in that The step of determining the target weight vector based on the interval relationship matrix includes: Determine a weighted weight vector of weight vectors corresponding to M differences that are less than or equal to the first preset difference threshold, where M is an integer greater than 1; Determining a weight vector difference between the weighted weight vector and a historical weighted weight vector, wherein the historical weighted weight vector is a weight vector of the most recently determined N weight vectors, the N weight vectors being the front weight vectors among the M weight vectors corresponding to differences that are 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 used as the target weight vector.

5. The method according to claim 4, characterized in that The method further comprises: If the weight vector difference is greater than the second preset difference threshold, determining a weighted weight vector of M+1 weight vectors corresponding to differences that are less than or equal to the first preset difference threshold; Taking M+1 as M, and repeatedly performing the step of determining the weight vector difference between the weighted weight vector and the historical weighted weight vector until a weighted weight vector corresponding to a weight vector difference that is less than or equal to the second preset difference threshold is determined; The weighted weight vector corresponding to the determined weight vector difference that is less than or equal to the second preset difference threshold is used as the target weight vector.

6. A reliability allocation device, characterized in that: The device comprises: A first determination module is used to determine a target weight vector based on an interval relationship matrix; A first acquisition module, configured to acquire a first distribution indicator of reliability of equipment in the equipment system; The second determination module is used to determine the second allocation index of the reliability of the first-level system in the equipment system according to the first relationship, wherein the first relationship is the relationship between the target weight vector, the first allocation index and the second allocation index; the first relationship includes ,in, are the elements in the target weight vector, is the first allocation indicator, is the second allocation index, i is greater than or equal to 1 and less than or equal to n, and n is the number of the next level; The device is further used for: Obtaining a reliability relationship between the primary system and each subsystem of the primary system; Determining, according to the target weight vector, a minimum element value among the element values in the target weight vector; Determine a third allocation 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 relational expression between the reliability relationship, the second allocation index, the ratio of each element value in the target weight vector to the minimum element value, and the third allocation index; the second relational expression includes , is the third allocation index, R is the reliability relationship, is the minimum element value, ; According to the third relational expression, a fourth distribution index of the reliability of each subsystem in the other subsystems is determined, wherein the third relational expression is a relational expression between the third distribution index, the fourth distribution index, and the ratio of the element value corresponding to the subsystem to the minimum element value, and the other subsystems include the systems in the subsystems except the subsystem corresponding to the minimum element value; the third relational expression includes , allocating an index for the fourth; The device is also used to obtain multiple scoring results, each of which is a result obtained by scoring multiple constraints of the equipment system; the constraints include complexity, importance, technical level, environmental conditions or working hours; based on each of the scoring results, a corresponding priority relationship matrix is determined; based on the priority relationship matrix corresponding to each of the scoring results, the interval relationship matrix is determined.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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