Reusable spaceflight equipment reliability distribution method, equipment, medium and product

Abstract

The invention discloses a reliability distribution method, equipment, medium and product for reusable aerospace equipment, and relates to the field of reliability distribution of the reusable aerospace equipment, and the method comprises the steps: obtaining the failure rate and service age rollback data of each subsystem; determining a reliability function of the reusable aerospace equipment according to the failure rate of each subsystem and the service age rollback data; a multi-objective optimization model is constructed according to the repeated use operation and maintenance cost; determining a designer preference structure model by using an artificial neural network based on a designer preference framework of a Pareto boundary; the reliability distribution value of the highest preference value output by the designer preference structure model is used as an objective function, the multi-objective optimization model is optimized, and a reliability distribution scheme of the reusable aerospace equipment is obtained; the method can improve the reliability and benefit of the reusable spaceflight equipment.

Description

Technical Field

[0001] The present application relates to the field of reliability allocation of reusable aerospace equipment, and in particular to a reliability allocation method, equipment, medium and product for reusable aerospace equipment. Background Art

[0002] In recent years, the aerospace industry has flourished, constantly refreshing the height of innovation and development with outstanding achievements. Compared with traditional disposable spacecraft, reusable aerospace equipment has the characteristics of reusability, cost reduction, efficiency improvement, and promotion of aerospace technology innovation. However, reusable aerospace equipment has the characteristics of high performance, high value, small batch, complex composition structure, and harsh operating environment. Therefore, in the design and development process, the reasonable distribution of reliability has always been an important issue in the design and development of reusable aerospace equipment.

[0003] From the perspective of design indicators, the design of reusable aerospace equipment must focus on cost-effectiveness. This means that economic constraints must be fully considered in the design process to ensure that reusable aerospace equipment can significantly reduce costs, shorten the fulfillment cycle, and reduce dependence on production capacity through sufficient reuse. This requires that the reuse problem and the life cycle operation and maintenance management problem of reusable aerospace equipment must be solved in the design. In order to meet these new challenges, reusable aerospace equipment technology cannot be simply regarded as a simple superposition of disposable launch vehicle engine technology and reusable engine technology. Similarly, it is not possible to simply improve on the existing design methods and systems. On the contrary, a new design concept needs to be adopted to build the design of reusable aerospace equipment from the bottom up. In summary, there is an urgent need for a reliability allocation method for reusable aerospace equipment to cope with the increasingly complex equipment design and improve the accuracy and efficiency of reliability allocation. Summary of the invention

[0004] The purpose of this application is to provide a method, device, medium and product for reusable aerospace equipment reliability allocation, which can improve the reliability and efficiency of reusable aerospace equipment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for allocating the reliability of reusable aerospace equipment, the method comprising:

[0007] Divide reusable aerospace equipment into multiple subsystems;

[0008] Obtain failure rate and age-reduction data for each subsystem;

[0009] Determine the reliability function of reusable aerospace equipment based on the failure rate and service age regression data of each subsystem;

[0010] Construct a multi-objective optimization model based on the reliability function of reused aerospace equipment and the reused operation and maintenance cost;

[0011] Based on the designer preference framework of Pareto frontier, an artificial neural network is used to determine a designer preference structure model; the designer preference structure model is used to output the reliability distribution value of each subsystem considering the designer preference;

[0012] Taking the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, the multi-objective optimization model is optimized to obtain the reliability allocation scheme of reusable aerospace equipment; the reliability allocation scheme includes the reliability allocation value of each subsystem, the overall reliability of the reusable aerospace equipment, and the reusable operation and maintenance cost.

[0013] Optionally, the obtaining of failure rate and service age regression data of each subsystem specifically includes:

[0014] Accelerated experiments were conducted on key components of each subsystem, and the formula Determine the age regression data of the i-th subsystem when it is reused for the Tth time Among them, a i is the structural parameter of the ith subsystem, a i T is the structural parameter of the ith subsystem when it is reused for the Tth time, and e is the natural logarithm.

[0015] Optionally, determining the reliability function of the reusable aerospace equipment according to the failure rate and service age regression data of each subsystem specifically includes:

[0016] Using the formula Determine the reliability function of reusable aerospace equipment;

[0017] Among them, R(T) is the reliability of reusable aerospace equipment after being reused T times, is the initial reliability value of the ith subsystem, Tn is the number of repeated uses, n is the number of subsystems, It is the service life fallback data of the i-th subsystem when it is reused for the Tth time.

[0018] Optionally, the multi-objective optimization model is constructed according to the reliability function of the reused aerospace equipment and the reused operation and maintenance cost, specifically including:

[0019] Using the formula Determine the objective function of the multi-objective optimization model;

[0020] Using the formula Determine the constraints of the multi-objective optimization model;

[0021] Among them, C d Costs related to the maintenance process; C m is the maintenance cost of the whole life cycle, C m is the lifetime maintenance cost, C s is the cost related to the maintenance of the whole life cycle, C is the cost of repeated use and maintenance, R u The minimum reliability allowed for reusable aerospace equipment, C o is the maximum value of the product's life cycle maintainability-related costs, C i is the reuse operation and maintenance cost of the i-th subsystem.

[0022] Optionally, based on the designer preference framework of the Pareto frontier, an artificial neural network is used to determine the designer preference structure model, specifically including:

[0023] Using the formula Determine the weight vector space Λ when iterating n times n ; The weight is used to characterize the reliability allocation value;

[0024] in, and Respectively represent the weight ω of the i-th subsystem when iterates n times i are the upper and lower bounds of , and ω is the input weight vector.

[0025] Optionally, the reliability allocation value of the highest preference value output by the designer preference structure model is used as the objective function, and the multi-objective optimization model is optimized to obtain a reliability allocation scheme for reusable aerospace equipment, specifically including:

[0026] Using the formula Determine the objective function of the reliability allocation value of the highest preference value output by the designer preference structure model; wherein ANN(ω) represents the preference structure when the input weight vector is ω calculated using the designer preference structure model;

[0027] The NSGA-II algorithm is used to optimize the multi-objective optimization model.

[0028] In a second aspect, the present application provides a device for reusing aerospace equipment reliability allocation, the device for reusing aerospace equipment reliability allocation comprising:

[0029] A partitioning module is used to divide reusable aerospace equipment into multiple subsystems;

[0030] Data acquisition module, used to obtain the failure rate and service age regression data of each subsystem;

[0031] A reliability function determination module is used to determine the reliability function of the reusable aerospace equipment according to the failure rate of each subsystem and the service age regression data;

[0032] A multi-objective optimization model building module is used to build a multi-objective optimization model based on the reliability function of reused aerospace equipment and the reused operation and maintenance cost;

[0033] A designer preference structure model determination module is used to determine the designer preference structure model based on the designer preference framework of the Pareto boundary by using an artificial neural network; the designer preference structure model is used to output the reliability distribution value of each subsystem considering the designer preference;

[0034] The reliability allocation scheme determination module is used to optimize the multi-objective optimization model with the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, so as to obtain the reliability allocation scheme of the reusable aerospace equipment; the reliability allocation scheme includes the reliability allocation value of each subsystem, the overall reliability of the reusable aerospace equipment and the reusable operation and maintenance cost.

[0035] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the reliability allocation method for reusable aerospace equipment.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reliability allocation method for reusable aerospace equipment.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the reliability allocation method for reusable aerospace equipment.

[0038] According to the specific embodiments provided in this application, this application has the following technical effects:

[0039] The present application provides a reliability allocation method, equipment, medium and product for reusable aerospace equipment. First, the failure rate and service age regression data of the reusable aerospace equipment subsystems are collected to establish a multi-objective optimization model that considers the operation and maintenance cost and reliability of the aerospace engine. Then, based on the designer preference framework of the Pareto frontier, the designer preference structure model is determined using an artificial neural network. Finally, the reliability allocation value with the highest preference value output by the designer preference structure model is used as the objective function to optimize the multi-objective optimization model and obtain a reliability allocation plan for reusable aerospace equipment. The present application can handle the uncertainty of the reliability assessment of reusable aerospace equipment, provide a decision-making means for the reliability allocation of reusable aerospace equipment, effectively solve the multi-objective allocation of reliability indicators of reusable aerospace equipment, maximize the reliability of reusable aerospace equipment, and improve the economic benefits of reusable aerospace equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 This is a flow chart of a reliability allocation method for reusable aerospace equipment in one embodiment of the present application;

[0042] Figure 2 This is a schematic diagram of a case of reusing aerospace equipment structure in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of iteratively generating a Pareto optimal solution for multi-objective optimization in an embodiment of the present invention;

[0044] Figure 4 Schematic diagram of a neural network framework based on designer preferences in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0047] In an exemplary embodiment, Figure 1 As shown, a method for reusable aerospace equipment reliability allocation is provided, the method comprising the following S101 to S106. Wherein:

[0048] S101, divide reusable aerospace equipment into multiple subsystems;

[0049] S102, obtaining the failure rate and service age regression data of each subsystem;

[0050] Accelerated experiments were conducted on key components of each subsystem, and the formula Determine the age regression data of the i-th subsystem when it is reused for the Tth time Among them, a i is the structural parameter of the ith subsystem, a i T is the structural parameter of the ith subsystem when it is reused for the Tth time, and e is the natural logarithm.

[0051] With the increase of service time and the number of overhauls, the effect of maintenance on the service life of the equipment becomes smaller and smaller, showing the phenomenon of repair fatigue.

[0052] like Figure 2 As shown, this embodiment uses a reusable aerospace engine structure including a two-stage subsystem. Taking 10 times of reuse as the design basis, the failure rate and service age regression data of the thrust chamber, turbo pump, gas generator and starter are obtained through accelerated tests;

[0053] S103, determining the reliability function of the reusable aerospace equipment according to the failure rate and service age regression data of each subsystem;

[0054] This application assumes that the regression increment changes exponentially. By integrating the service age regression data and the failure rate (service age factor), a comprehensive system reliability model can be constructed. The system reliability model not only covers the inherent reliability of the system, but also considers the reliability status after maintenance.

[0055]

[0056] Among them, D f is the failure threshold of the subsystem, φ is the cumulative distribution function of the standard normal distribution, is the diffusion coefficient, is the drift coefficient, It is the service life rollback data of the i-th subsystem when it is reused for the Tth time, where T is the number of reuses.

[0057] S103 specifically includes:

[0058] Using the formula Determine the reliability function of reusable aerospace equipment;

[0059] Among them, R(T) is the reliability of reusable aerospace equipment after being reused T times, is the initial reliability value of the ith subsystem, Tn is the number of repeated uses, n is the number of subsystems, It is the service life fallback data of the i-th subsystem when it is reused for the Tth time.

[0060] The failure rate, weight, service age regression data, and operation and maintenance cost evaluation results of each subsystem are shown in Table 1, where the weight of each subsystem ω i (i=1,2,3,4), randomly generated, and The service age regression data increases with the increase of the number of reuse times T, showing an exponential distribution.

[0061] Table 1

[0062]

[0063]

[0064] S104, constructing a multi-objective optimization model based on the reliability function of reused aerospace equipment and the reused operation and maintenance cost;

[0065] In the reliability allocation of reusable aerospace equipment, the system reliability model and the operation and maintenance cost model are both related to the weights allocated to each subsystem in the initial state. Therefore, this application establishes a mathematical model that comprehensively considers the reliability and operation and maintenance costs of multi-stage reusable aerospace equipment systems to make a trade-off. The mathematical model is:

[0066]

[0067] Among them, x is the decision vector, is the weight vector of the subsystem, f1(x) and f2(x) are the two objective functions of the reliability and operation and maintenance cost of the aerospace engine in the reuse stage respectively; g1(x) and g2(x) are the constraints of the reliability and operation and maintenance cost of the reused aerospace equipment respectively.

[0068] Mathematical function of operation and maintenance related costs during the whole cycle of reused aerospace equipment:

[0069]

[0070] Among them, C d Costs related to the maintenance process; C m is the maintenance cost of the whole life cycle; C mi The life cycle maintenance cost of the ith maintenance unit; C 1iis the life-cycle corrective maintenance cost of the ith maintenance unit; C 2i is the system downtime loss caused by the failure of the i-th maintenance unit: C ui is the unit time cost of corrective maintenance of the ith maintenance unit; C ii is the loss of production stoppage per unit time of the product; C s Costs related to maintainability throughout the life cycle.

[0071] Furthermore, the objective function describing the reliability and operation and maintenance cost of the reusable aerospace equipment system can be derived as follows:

[0072]

[0073] Constraints of the multi-objective optimization model:

[0074]

[0075] Among them, C d Costs related to the maintenance process; C m is the maintenance cost of the whole life cycle, C m is the lifetime maintenance cost, C s is the cost related to the maintenance of the whole life cycle, C is the cost of repeated use and maintenance, R u The minimum reliability allowed for reusable aerospace equipment, C o is the maximum value of the product's life cycle maintainability-related costs, C i is the reuse operation and maintenance cost of the i-th subsystem.

[0076] S105, based on the designer preference framework of the Pareto frontier, using an artificial neural network, determining a designer preference structure model; the designer preference structure model is used to output a reliability distribution value of each subsystem considering the designer preference; the designer preference structure model obtains the weight of the relevant subsystem in the online decision-making stage;

[0077] The offline training phase of the designer's preference structure model collects training data sets in interaction with the preference scoring, uses the proximal strategy optimization method to train the intelligent agent, and optimizes the neural network parameters.

[0078] S105 specifically includes:

[0079] Construct the weight vector [ω1,ω2,...,ω m ] as the model parameter vector;

[0080] Using the formula Determine the weight vector space Λ when iterating n times n ; The weight is used to characterize the reliability allocation value;

[0081] in, and Respectively represent the weight ω of the i-th subsystem when iterates n times i The upper and lower bounds of ω are as follows: make

[0082] S106, taking the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, optimizing the multi-objective optimization model, and obtaining a reliability allocation scheme for reusable aerospace equipment; the reliability allocation scheme includes the reliability allocation value of each subsystem, the overall reliability of the reusable aerospace equipment, and the reusable operation and maintenance cost.

[0083] S106 specifically includes:

[0084] Using the formula Determine the objective function of the reliability allocation value of the highest preference value output by the designer preference structure model; wherein ANN(ω) represents the preference structure when the input weight vector is ω calculated using the designer preference structure model;

[0085] In the weight vector space, K weight vectors are randomly generated. If T>1, the first weight vector is replaced by the best weight vector ω obtained in the previous iteration T-1 The optimal weight vector ω T-1 It is obtained by solving the optimization problem through the designer preference structure model. Some of the generated Pareto solutions are shown in Table 2.

[0086] Table 2

[0087]

[0088] The designer preference structure model takes the model parameter vector as input and the preference information expressed by the designer on the Pareto frontier for representative samples as the expected output, and uses an artificial neural network to establish the designer preference structure model. Then, the designer preference structure model is used to train the feedforward neural network with the iterative Pareto solution as the training set, the weight vector as input, and the corresponding preference value as the expected output, to obtain the designer preference structure model. The structure diagram of the designer preference structure model is shown in the figure below: Figure 4 As shown, the input layer contains four neurons, the output layer has one neuron, and the hidden layer has ten neurons. In the case of a training set with four input neurons and one output neuron, this artificial neural network is generally regarded as a model with a compact structure and good performance.

[0089] The NSGA-II algorithm is used to optimize the multi-objective optimization model.

[0090] After clarifying the multi-objective optimization goals and constraints, NSGA-II is used for multi-objective optimization. First, the fitness of each individual is quantified in the evaluation phase to determine their performance in the problem domain; then, based on the evaluation results, the selection phase selects suitable parent individuals from the basic population to generate the next generation; finally, in the modification phase, the newly generated offspring individuals undergo genetic operations such as crossover and mutation to simulate the evolutionary process in nature, thereby generating a new generation of populations.

[0091] Assume that the population size at the beginning is P0, the maximum number of generations is M, and the population of the tth generation is P t , the elite population is P s .

[0092] Step 1: Initialize n solutions, generate the initial population P0, and set the number of iterations t=0.

[0093] Step 2: Use binary coding and tournament method to select P t Each individual proceeds to the next step.

[0094] Step 3: Perform crossover and mutation operations on the selected individuals to generate new individuals and enter Q t .

[0095] Step 4: Calculate P t and Q t The fitness of individuals in .

[0096] Step 5: P t and Q t The elite individuals in P s , if the number of elite individuals does not exceed P s The scale of P t and Q t The elite individuals among the remaining individuals are stored in P s . And so on, until the scale exceeds P s The maximum size. At this time, the truncation algorithm is used to calculate the crowding distance of individuals in the elite population for the last time, and then select individuals with large crowding distance to enter P first. t+1 , until the scale equals N.

[0097] Step 6: If t<T, then t=t+1 and go to Step 2. Otherwise, P t+1 The individual is output as the result; the iterative process is as follows Figure 3As shown in the figure, the generated Pareto solution is used as the training set to train the designer preference structure model. The specific training parameters are set as follows: maximum number of iterations: 1000; target error: 0.01; learning rate: 0.01, and the data set is divided into 70% as the training set, 15% as the validation set, and 15% as the test set.

[0098] The reliability allocation scheme for reusable aerospace equipment in this embodiment is shown in Table 3. The weights of thrust chamber, turbo pump, gas generator and starter are ω ideal =[0.42 0.21 0.29 0.18];

[0099] Table 3

[0100]

[0101] Based on the same inventive concept, the embodiment of the present application also provides a device for reusing aerospace equipment reliability allocation for implementing the above-mentioned method for reusing aerospace equipment reliability allocation. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for reusing aerospace equipment reliability allocation provided below can refer to the limitations of the method for reusing aerospace equipment reliability allocation above, and will not be repeated here.

[0102] In an exemplary embodiment, a reliability allocation device for reusable aerospace equipment is provided, comprising:

[0103] A partitioning module is used to divide reusable aerospace equipment into multiple subsystems;

[0104] Data acquisition module, used to obtain the failure rate and service age regression data of each subsystem;

[0105] A reliability function determination module is used to determine the reliability function of the reusable aerospace equipment according to the failure rate of each subsystem and the service age regression data;

[0106] A multi-objective optimization model building module is used to build a multi-objective optimization model based on the reliability function of reused aerospace equipment and the reused operation and maintenance cost;

[0107] A designer preference structure model determination module is used to determine the designer preference structure model based on the designer preference framework of the Pareto boundary by using an artificial neural network; the designer preference structure model is used to output the reliability distribution value of each subsystem considering the designer preference;

[0108] The reliability allocation scheme determination module is used to optimize the multi-objective optimization model with the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, so as to obtain the reliability allocation scheme of the reusable aerospace equipment; the reliability allocation scheme includes the reliability allocation value of each subsystem, the overall reliability of the reusable aerospace equipment and the reusable operation and maintenance cost.

[0109] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a reliability allocation method for reusable aerospace equipment is implemented.

[0110] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0111] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0112] 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, and the collection, use and processing of relevant data must comply with relevant regulations.

[0113] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can 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 can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0114] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0115] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0116] The technical features of the above embodiments may 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.

[0117] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A reliability allocation method for reusable aerospace equipment, characterized in that: The reliability allocation method for reusable aerospace equipment includes: Divide reusable aerospace equipment into multiple subsystems; Obtain failure rate and age-reduction data for each subsystem; Determine the reliability function of reusable aerospace equipment based on the failure rate and service age regression data of each subsystem; Construct a multi-objective optimization model based on the reliability function of reused aerospace equipment and the reused operation and maintenance cost; Based on the designer preference framework of Pareto frontier, an artificial neural network is used to determine a designer preference structure model; the designer preference structure model is used to output the reliability distribution value of each subsystem considering the designer preference; Taking the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, the multi-objective optimization model is optimized to obtain the reliability allocation scheme of reusable aerospace equipment; the reliability allocation scheme includes the reliability allocation value of each subsystem, the overall reliability of the reusable aerospace equipment, and the reusable operation and maintenance cost.

2. The reliability allocation method for reusable aerospace equipment according to claim 1, characterized in that: The obtaining of the failure rate and service age regression data of each subsystem specifically includes: Accelerated experiments were conducted on key components of each subsystem, and the formula Determine the age regression data of the i-th subsystem when it is reused for the Tth time Among them, a i is the structural parameter of the ith subsystem, aiT is the structural parameter of the ith subsystem when it is reused for the Tth time, and e is the natural logarithm.

3. The reliability allocation method for reusable aerospace equipment according to claim 1, characterized in that: Determining the reliability function of the reusable aerospace equipment according to the failure rate and service age regression data of each subsystem specifically includes: Using the formula Determine the reliability function of reusable aerospace equipment; Among them, R(T) is the reliability of reusable aerospace equipment after being reused T times, is the initial reliability value of the ith subsystem, Tn is the number of repeated uses, n is the number of subsystems, It is the service life fallback data of the i-th subsystem when it is reused for the Tth time.

4. The reliability allocation method for reusable aerospace equipment according to claim 3, characterized in that: The multi-objective optimization model is constructed based on the reliability function of the reused aerospace equipment and the reused operation and maintenance cost, specifically including: Using the formula Determine the objective function of the multi-objective optimization model; Using the formula Determine the constraints of the multi-objective optimization model; Among them, C d Costs related to the maintenance process; C m is the maintenance cost of the whole life cycle, C m is the lifetime maintenance cost, C s is the cost related to the maintenance of the whole life cycle, C is the cost of repeated use and maintenance, R u The minimum reliability allowed for reusable aerospace equipment, C o is the maximum value of the product's life cycle maintainability-related costs, C i is the reuse operation and maintenance cost of the i-th subsystem.

5. The reliability allocation method for reusable aerospace equipment according to claim 1, characterized in that: Based on the Pareto frontier designer preference framework, artificial neural networks are used to determine the designer preference structure model, which includes: Using the formula Determine the weight vector space Λ when iterating n times n ; The weight is used to characterize the reliability allocation value; in, and Respectively represent the weight ω of the i-th subsystem when iterates n times i are the upper and lower bounds of , and ω is the input weight vector.

6. The reliability allocation method for reusable aerospace equipment according to claim 5, characterized in that: Taking the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, the multi-objective optimization model is optimized to obtain the reliability allocation scheme of reusable aerospace equipment, which includes: Using the formula Determine the objective function of the reliability allocation value of the highest preference value output by the designer preference structure model; wherein ANN(ω) represents the preference structure when the input weight vector is ω calculated using the designer preference structure model; The NSGA-II algorithm is used to optimize the multi-objective optimization model.

7. A reliability allocation device for reusable aerospace equipment, characterized in that: The reliability allocation device for reusable aerospace equipment includes: A partitioning module is used to divide reusable aerospace equipment into multiple subsystems; Data acquisition module, used to obtain the failure rate and service age regression data of each subsystem; A reliability function determination module is used to determine the reliability function of the reusable aerospace equipment according to the failure rate of each subsystem and the service age regression data; A multi-objective optimization model building module is used to build a multi-objective optimization model based on the reliability function of reused aerospace equipment and the reused operation and maintenance cost; A designer preference structure model determination module is used to determine the designer preference structure model based on the designer preference framework of the Pareto boundary by using an artificial neural network; the designer preference structure model is used to output the reliability distribution value of each subsystem considering the designer preference; The reliability allocation scheme determination module is used to optimize the multi-objective optimization model with the reliability allocation value of the highest preference value output by the designer preference structure model as the objective function, so as to obtain the reliability allocation scheme of the reusable aerospace equipment; the reliability allocation scheme includes the reliability allocation value of each subsystem, the overall reliability of the reusable aerospace equipment and the reusable operation and maintenance cost.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the reliability allocation method for reusable aerospace equipment as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for allocating the reliability of reusable aerospace equipment described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for allocating the reliability of reusable aerospace equipment described in any one of claims 1 to 6 is implemented.

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