Task success rate-oriented limited space equipment spare parts determination method

Abstract

The application discloses a task success rate-oriented limited space equipment spare part determination method, comprising the following steps: establishing an equipment failure time model based on replaceable unit failure time; single simulation, spare part list supplement; iterative simulation, final determination of complete spare part list. The application solves the problem that the type and quantity of spare parts cannot be reasonably determined under the premise of limited space.

Description

Technical Field

[0001] This invention belongs to the field of aerospace system design and support engineering technology, and particularly relates to a method for determining spare parts for confined space equipment based on mission success rate. Background Technology

[0002] In the past, the usage scenarios and operational ranges of most equipment were relatively limited, and high requirements were not placed on the storage and transportation of resources, especially spare parts. Generally, equipment users would store spare parts in fixed locations, and when a replaceable unit in the equipment was detected to have failed, the equipment's reliability would be restored by promptly replacing the spare part. However, with the diversification of equipment usage scenarios and the continuous expansion of its geographical scope, the original fixed-point spare parts warehouse support method can no longer meet the new support needs, and the inability to provide spare parts in a timely manner makes it difficult to guarantee the reliability of equipment. Therefore, some equipment has adopted a mobile spare parts warehouse support method, which has solved the spare parts supply problem to some extent.

[0003] Mobile spare parts depots differ from traditional fixed-location spare parts depots, especially in terms of space constraints. This presents a significant challenge: how to rationally determine the types and quantities of spare parts across all replaceable units.

[0004] In traditional fixed-point spare parts support, there is ample space available for spare parts, with a certain number of spare parts available for all replaceable units. The specific number is generally determined based on the spare parts availability requirements. However, under the premise of limited space, it is impossible to carry all types of spare parts, and the number of spare parts calculated based on the spare parts availability rate may be too large, potentially leading to a situation where the spare parts space cannot accommodate them. Summary of the Invention

[0005] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a method for determining spare parts for equipment in a limited space with a focus on mission success rate, thus solving the planning problem of spare parts types and quantities under limited space conditions.

[0006] The objective of this invention is achieved through the following technical solution: a method for determining spare parts for equipment in a limited space, oriented towards mission success rate, comprising: establishing an equipment failure time model based on the failure time of replaceable units; performing Monte Carlo simulation on each replaceable component affecting the equipment mission to obtain the failure time of each replaceable component, and then combining the equipment failure time model to determine whether the equipment mission is normal or not, prioritizing the replaceable component that first causes the equipment mission to fail to execute normally in the spare parts list; based on the current spare parts list, repeatedly performing Monte Carlo simulation on each replaceable component affecting the equipment mission to obtain the replaceable components that should be prioritized in the spare parts list, and determining whether the space occupied by the spare parts has reached the upper limit requirement; if the space occupied by the spare parts has not reached the upper limit requirement, repeatedly performing Monte Carlo simulation on each replaceable component affecting the equipment mission to obtain the replaceable components that should be prioritized in the spare parts list, until the space occupied by the spare parts reaches the upper limit requirement; if the space occupied by the spare parts reaches the upper limit requirement, then outputting the types and quantities of equipment spare parts under limited space conditions, which is the final determined spare parts list.

[0007] The aforementioned method for determining spare parts for confined space equipment based on mission success rate also includes: performing Monte Carlo simulation on the normal mission execution time of the equipment based on the final determined spare parts list and the equipment failure time model, and calculating and outputting the expected mission success rate.

[0008] In the above-mentioned method for determining spare parts for confined space equipment based on mission success rate, the equipment failure time is the failure time when the replaceable component fails, causing the equipment to be unable to perform the mission normally.

[0009] In the above-mentioned method for determining spare parts for confined space equipment based on mission success rate, the average of multiple simulated failure times for each replaceable component is used as the failure time of each replaceable component.

[0010] In the above-mentioned method for determining spare parts for confined space equipment based on mission success rate, the number of simulations of failure time for each replaceable component is the same; the calculation formula for each set of simulation failure time for each replaceable component follows an exponential distribution or a Weibull distribution.

[0011] In the above-mentioned method for determining spare parts for confined space equipment based on mission success rate, the replaceable component that first causes the equipment mission to fail to execute normally is the replaceable component whose failure time is the same as the equipment failure time calculated based on the equipment failure time model.

[0012] In the above-mentioned method for determining spare parts for limited space equipment based on mission success rate, the space occupied by the spare parts is the sum of the product of the quantity of all types of spare parts in the currently determined spare parts list and the space occupied by a single spare part.

[0013] In the above-mentioned method for determining spare parts for confined space equipment based on mission success rate, the expected mission success rate is determined by the proportion of times when the equipment performs its normal mission for longer than the specified mission time.

[0014] In the above-mentioned method for determining spare parts for confined space equipment based on mission success rate, the normal mission execution time of the equipment refers to the equipment failure time calculated based on the equipment failure time model.

[0015] A confined-space equipment spare parts determination system for mission success rate includes: a first module for establishing an equipment failure time model based on the failure time of replaceable units; a second module for performing Monte Carlo simulation on each replaceable component affecting the equipment mission, obtaining the failure time of each replaceable component, and then combining the equipment failure time model to determine whether the equipment mission is normal or not, prioritizing the replaceable component that first causes the equipment mission to fail to execute normally in the spare parts list; a third module for repeatedly performing Monte Carlo simulation on each replaceable component affecting the equipment mission based on the current spare parts list, obtaining the replaceable components to be prioritized in the spare parts list, and determining whether the space occupied by the spare parts has reached the upper limit requirement; a fourth module for repeatedly performing Monte Carlo simulation on each replaceable component affecting the equipment mission if the space occupied by the spare parts has not reached the upper limit requirement, obtaining the replaceable components to be prioritized in the spare parts list, until the space occupied by the spare parts reaches the upper limit requirement; if the space occupied by the spare parts reaches the upper limit requirement, the system outputs the types and quantities of equipment spare parts under confined space conditions, which is the final determined spare parts list.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] (1) This invention can solve the problem of not being able to reasonably determine the type and quantity of spare parts under the premise of limited space, effectively provide a method for determining spare parts for equipment in limited space oriented towards mission success rate, and realize mission reliability prediction under the spare parts list;

[0018] (2) This invention establishes a process for determining spare parts for equipment in confined spaces, oriented towards mission success rate. The spare parts list is determined step-by-step through multiple iterative simulations, solving the problem of determining the types and quantities of spare parts within a confined space.

[0019] (3) This invention constructs an equipment failure time model and establishes the relationship between equipment failure time and replaceable unit failure time, laying the foundation for determining the replaceable unit that fails first and realizing the prediction of equipment mission success rate.

[0020] (4) This invention develops a method for predicting the failure time of replaceable units. By using the Monte Carlo simulation method and combining it with the task reliability block diagram, the problem of the inability to theoretically calculate the reliability level of complex models is solved, and the unit failure time prediction is realized when parallel models (hot standby) and side-connected models (cold standby) exist simultaneously. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 This is a schematic diagram of the process for determining spare parts for confined space equipment based on mission success rate, provided in an embodiment of the present invention.

[0023] Figure 2 This is a reliability block diagram provided in an embodiment of the present invention. Detailed Implementation

[0024] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] Figure 1 This is a schematic diagram of the process for determining spare parts for confined space equipment based on mission success rate, provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0026] Establish an equipment failure time model based on the failure time of replaceable units; wherein, the replaceable components that affect the equipment mission are identified, and the equipment failure time model based on the failure time of replaceable units is established according to the logical relationship between the normal operation of the equipment mission and the replaceable components.

[0027] Monte Carlo simulation is performed on each replaceable component that affects the equipment mission to obtain the failure time of each replaceable component. Then, the failure time model of the equipment is combined to determine whether the equipment mission is normal or not. Replaceable components that first cause the equipment mission to fail to be executed normally are given priority to be included in the spare parts list.

[0028] Based on the current spare parts list, repeat Monte Carlo simulation for each replaceable component that affects the equipment mission to obtain the replaceable components that should be prioritized in the spare parts list and determine whether the space occupied by the spare parts has reached the upper limit requirement.

[0029] If the space occupied by spare parts does not reach the upper limit requirement, repeat the Monte Carlo simulation for each replaceable component that affects the equipment mission to obtain the replaceable components that should be prioritized for inclusion in the spare parts list, until the space occupied by spare parts reaches the upper limit requirement; if the space occupied by spare parts reaches the upper limit requirement, output the types and quantities of equipment spare parts under the limited space conditions, which is the final determined spare parts list.

[0030] The method also includes: performing Monte Carlo simulations on the normal mission execution time of the equipment based on the finalized spare parts list and the equipment failure time model, calculating and outputting the predicted mission success rate. The predicted mission success rate is determined by the proportion of times the equipment's normal mission execution time exceeds the specified mission time. The normal mission execution time refers to the equipment failure time calculated based on the equipment failure time model.

[0031] Equipment failure time is the time when a replaceable component fails, causing the equipment to be unable to perform its mission normally.

[0032] The failure time of each component includes: the average of multiple simulated failure times of a certain component as the failure time of that component.

[0033] In a failure time model where the average of multiple simulated failure times for a given component is used as the failure time of that component, the number of simulations for each component is the same. The calculation formula for each simulated failure time of each component is determined based on the distribution type followed by the product, which is generally an exponential distribution or a Weibull distribution.

[0034] The component that first causes the equipment mission to fail to perform normally refers to the component whose failure time is the same as the equipment failure time calculated based on the equipment failure time model.

[0035] Spare parts space refers to the sum of the products of the quantity of all types of spare parts in the currently determined spare parts list and the space occupied by a single spare part.

[0036] Specifically, the method includes the following steps:

[0037] (1) Construct an equipment failure time model based on the failure time of replaceable units.

[0038] When assessing mission reliability, a mission reliability model must be established, which generally includes a mission reliability diagram and mathematical expressions. Based on the mission reliability diagram, we can clearly show the logical relationship between mission success or failure and the normal functioning of each component, thereby further obtaining a mathematical model between the time when the equipment can perform the mission normally and the failure time of each replaceable unit.

[0039] In a series model, the time it takes for the equipment to perform a task normally is:

[0040] T s =min(T1,T2,T3,...,T) n )

[0041] In the formula,

[0042] T s —The time during which the equipment can perform its mission normally;

[0043] T i —The failure time of the i-th serial unit;

[0044] n—The number of units in the cascade model.

[0045] Similarly, in the parallel model, the time it takes for the equipment to perform the task normally is:

[0046] T s =max(T1,T2,T3,...,T) n )

[0047] In the formula,

[0048] T i —The failure time of the i-th parallel unit;

[0049] n — the number of units in the parallel model.

[0050] Other reliability models (such as the k / n model, the cross-connection model, etc.) can all be used to establish corresponding failure time models.

[0051] (2) Single simulation, supplement the spare parts list.

[0052] Based on the equipment failure time model, the failure time of the equipment can be predicted by simulating the failure time of replaceable units. The key is to locate the "shortcoming" that causes the equipment to fail, that is, the replaceable unit that fails first, and add it to the spare parts list.

[0053] In a single simulation, the Monte Carlo simulation method is used to predict the failure time of the replaceable unit. For electronic products, the predicted failure time is related to their mean time between failures (MTBF), and the simulation relationship is as follows:

[0054] T = -MTBF × ln(rand)

[0055] In the formula,

[0056] T—Random failure time;

[0057] rand is a random number between 0 and 1.

[0058] Since failure times have a certain degree of randomness, the statistical value of the random failure time can be obtained by generating multiple sets of random numbers, which is the predicted failure time. It should be noted that to reduce the error caused by randomness, the number of random numbers generated should not be too small.

[0059] For example, for a series model consisting of n units, failure time simulations are performed on each unit to predict the failure time, resulting in a set of failure times (T1, T2, T3, ..., T). n According to the failure time expression for the series model listed in the first step, its failure time is the minimum value among n T values. This method is also applicable to parallel models, n / k models, etc. It is worth noting that the failure time of simple models can be obtained through theoretical calculations, such as the failure time of a single-point element being the MTBF value; the failure time of a double-redundant element is 1.5 times the MTBF value of a single unit. However, for complex models, especially under conditions of redundancy and cold standby, the accurate failure time cannot be obtained through theoretical calculations, and the simulation method proposed in this paper is needed to obtain the predicted failure time.

[0060] After estimating the failure time of all replaceable units, the equipment failure time can be obtained based on the failure time model from the first step. This allows for the identification of the replaceable unit that will fail first, preventing the equipment from completing its mission. To extend the equipment's mission completion time, this unit must be added to the spare parts list, thus completing the process of supplementing the spare parts list for a single mission.

[0061] (3) Iterative simulation was conducted to determine the complete spare parts list.

[0062] When the spare parts list changes, the equipment failure time model also changes. The more spare parts a replaceable unit has, the longer its failure time will be. Therefore, after supplementing the spare parts list in the second step, it is necessary to re-estimate the failure time of each replaceable unit and the equipment failure time, and to re-identify the "weak links" in the current situation to determine the types of replaceable units that need to be added to the spare parts list.

[0063] Repeated simulations were conducted to identify failure time bottlenecks, and the spare parts list was continuously expanded until the space occupied by the spare parts reached its upper limit, at which point no more spare parts could be added. This yielded an effective space equipment spare parts list oriented towards mission success rate.

[0064] (4) Expected success rate of tasks within the specified time.

[0065] The Monte Carlo method is still used to predict the mission completion capability of the equipment under the current spare parts list. In a single simulation, the single random failure time of replaceable units (including spare parts) is first obtained based on random numbers. The equipment failure time under this simulation condition is calculated according to the equipment random failure time model, and this time is compared with the specified mission time. If the equipment failure time is greater than the specified mission time, the equipment can complete the mission under this simulation condition; otherwise, it is recorded as unable to complete the mission.

[0066] After multiple simulations, the number of times the equipment was able to complete the mission was counted in all simulations, and the proportion of these counts represents the predicted mission success rate of the equipment.

[0067] Example:

[0068] The present invention will be described in detail below through specific embodiments.

[0069] (1) Taking a series-parallel model as an example, the application of this patented method is explained. The reliability block diagram of the embodiment is as follows: Figure 2 As shown, the system consists of five types of units: units 1 and 3 are single-point, units 2 and 4 are dual-redundant, and unit 5 is triple-redundant. Under this model, the equipment can perform its mission normally within the following timeframe:

[0070] T s =min(T) s1 ,T s2 ,T s3 ,...T sn )

[0071] In the formula,

[0072] T s1 —The failure time of the first serial node, T s1 =T1, which is the failure time of unit 1;

[0073] T s2 —The failure time of the second cascaded node, T s2 =max(T) 2-1 ,T 2-2 (), which is the longer failure time among the two units 2;

[0074] T s3 —The failure time of the third cascaded node, T s3 =T3, which is the failure time of unit 3;

[0075] T s4 —The failure time of the fourth cascaded node, T s4=max(T) 4-1 ,T 4-2 (), which is the longer failure time among the two units 4;

[0076] T s5 —The failure time of the 5th cascade node, T s5 =max(T) 5-1 ,T 5-2 T 5-3 (), which is the longest failure time among the three units 5.

[0077] (2) Assume that the product information for the five replaceable units is shown in the table below.

[0078] Table 1 Replaceable Unit Information

[0079] Serial Number name MTBF / h Space occupied 1 Unit 1 5000 8 2 Unit 2 3000 10 3 Unit 3 6500 7 4 Unit 4 4500 5 5 Unit 5 3000 5

[0080] In the Monte Carlo simulation, the number of random number generation attempts was set to 5000. Based on the MTBF values ​​of each element, the predicted failure time T for element 1 was obtained. s1 =4998.8h, the estimated failure time under dual redundancy in Unit 2 is T s2 = 4508.4h, the estimated failure time of unit 3 is T s3 =6512.1h, the estimated failure time under dual redundancy in element 4 is T s4 =6746.2h, the estimated failure time under triple redundancy in element 5 is T s5 =5506.4h. The time during which the equipment can perform its mission normally is:

[0081] T s =min(T) s1 ,T s2 ,T s3 ,T s4 ,T s5 ) = T s2 =4508.4h

[0082] Therefore, Unit 2 has the shortest failure time and is the first "weak link" that causes the equipment to fail to complete its mission normally. Therefore, Unit 2 will be added to the spare parts list.

[0083] (3) In the second step, Unit 2 has been included in the spare parts list. Based on the current situation, the Monte Carlo simulation method is used again to predict the failure time of each replaceable unit. Since Unit 1, Unit 3, Unit 4 and Unit 5 have not changed, their predicted failure time values ​​can use the previous simulation results. The prediction is then made for Unit 2.

[0084] Unit 2 has been changed from a dual-redundancy model to a dual-redundancy, single-cold-standby model. Under the new operating conditions, two Unit 2 products initially operate simultaneously. If one fails, a spare part can be used to take over. This continues until both the replacement spare part and the other product fail, at which point Unit 2 completely fails, leading to equipment failure. Based on this logic, the Monte Carlo method is used to obtain the estimated failure time T for Unit 2. s2 =6024.6h. At this point, the equipment is capable of performing its mission normally for the following duration:

[0085] T s =min(T) s1 ,T s2 ,T s3 ,T s4 ,T s5 ) = T s1 =4998.8h

[0086] Therefore, Unit 1 has the shortest failure time and is the first "weak link" that causes the equipment to fail to complete its mission normally. Therefore, Unit 1 is added to the spare parts list. At this time, the spare parts list includes 1 Unit 1 and 1 Unit 2, occupying 18 space.

[0087] Repeat the above process until the spare parts space reaches its limit (assuming it is 50), and you will get a spare parts list under limited space. The process of changing the spare parts list is shown in the table below.

[0088] Table 2: Changes to the Spare Parts List

[0089]

[0090]

[0091] The final spare parts quantity for the five replaceable units was determined to be: one each for Unit 1, Unit 3, and Unit 4, and two each for Unit 2 and Unit 5, which reached the maximum spare parts space limit of 50.

[0092] (4) The number of spare parts for the 5 replaceable units has been determined. When the mission time is 2000h, after 10000 simulations, the equipment can complete the mission 8376 times. Therefore, the expected mission success rate of the equipment under the specified time is 0.8376.

[0093] This embodiment also provides a confined-space equipment spare parts determination system oriented towards mission success rate. The system includes: a first module for establishing an equipment failure time model based on the failure time of replaceable units; a second module for performing Monte Carlo simulations on each replaceable component affecting the equipment mission, obtaining the failure time of each replaceable component, and then combining this with the equipment failure time model to determine the normality of the equipment mission, prioritizing the replaceable component that first causes the equipment mission to fail to execute properly in the spare parts list; a third module for repeatedly performing Monte Carlo simulations on each replaceable component affecting the equipment mission based on the current spare parts list, obtaining the replaceable components to be prioritized for inclusion in the spare parts list, and determining whether the space occupied by the spare parts has reached the upper limit requirement; a fourth module for repeatedly performing Monte Carlo simulations on each replaceable component affecting the equipment mission if the space occupied by the spare parts has not reached the upper limit requirement, obtaining the replaceable components to be prioritized for inclusion in the spare parts list, until the space occupied by the spare parts reaches the upper limit requirement; if the space occupied by the spare parts reaches the upper limit requirement, then outputting the types and quantities of equipment spare parts under confined space conditions, i.e., the final determined spare parts list.

[0094] This embodiment addresses the challenge of determining the types and quantities of spare parts under limited space constraints, effectively providing a method for determining spare parts for equipment within limited space with a focus on mission success rate, and achieving mission reliability prediction under this spare parts list. This embodiment establishes a process for determining spare parts for equipment within limited space with a focus on mission success rate. Through multiple iterative simulations, the spare parts list is determined step-by-step, solving the problem of determining the types and quantities of spare parts within limited space. This embodiment constructs an equipment failure time model, establishing the relationship between equipment failure time and replaceable unit failure time, laying the foundation for determining replaceable units that fail first and predicting equipment mission success rate. This embodiment develops a method for predicting the failure time of replaceable units. Using Monte Carlo simulation combined with a mission reliability diagram, it solves the problem of complex models being unable to theoretically calculate reliability levels, achieving unit failure time prediction when parallel models (hot standby) and side-connected models (cold standby) coexist.

[0095] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A method for determining spare parts for equipment in a confined space based on mission success rate, characterized in that... include: Establish an equipment failure time model based on the failure time of replaceable units; Monte Carlo simulation is performed on each replaceable component that affects the equipment mission to obtain the failure time of each replaceable component. Then, the failure time model of the equipment is combined to determine whether the equipment mission is normal or not. Replaceable components that first cause the equipment mission to fail to be executed normally are given priority to be included in the spare parts list. Based on the current spare parts list, repeat Monte Carlo simulation for each replaceable component that affects the equipment mission to obtain the replaceable components that should be prioritized in the spare parts list and determine whether the space occupied by the spare parts has reached the upper limit requirement. If the space occupied by spare parts does not reach the upper limit requirement, repeat the Monte Carlo simulation for each replaceable component that affects the equipment mission to obtain the replaceable components that should be prioritized for inclusion in the spare parts list, until the space occupied by spare parts reaches the upper limit requirement. If the space occupied by spare parts reaches the upper limit requirement, the output will be the final spare parts list, which includes the types and quantities of equipment spare parts under limited space conditions.

2. The method for determining spare parts for confined space equipment based on mission success rate according to claim 1, characterized in that... It also includes: performing Monte Carlo simulations on the normal mission execution time of the equipment based on the finalized spare parts list and equipment failure time model, calculating and outputting the expected mission success rate.

3. The method for determining spare parts for confined space equipment based on mission success rate according to claim 1, characterized in that: Equipment failure time is the time when the equipment is unable to perform its mission normally due to the failure of a replaceable component.

4. The method for determining spare parts for confined space equipment based on mission success rate according to claim 1, characterized in that: The average of multiple simulated failure times for each replaceable component is taken as the failure time of each replaceable component.

5. The method for determining spare parts for confined space equipment based on mission success rate according to claim 4, characterized in that: The number of failure time simulations for each replaceable component is the same; the calculation formula for the failure time of each set of simulations for each replaceable component follows an exponential distribution or a Weibull distribution.

6. The method for determining spare parts for confined space equipment based on mission success rate according to claim 1, characterized in that: The first replaceable component that causes the equipment mission to fail to perform normally is the one whose failure time is the same as the equipment failure time calculated based on the equipment failure time model.

7. The method for determining spare parts for confined space equipment based on mission success rate according to claim 1, characterized in that: The space occupied by spare parts is the sum of the product of the quantity of all types of spare parts in the currently determined spare parts list and the space occupied by a single spare part.

8. The method for determining spare parts for confined space equipment based on mission success rate according to claim 2, characterized in that: The expected mission success rate is determined by the percentage of times when the equipment performs its mission normally for longer than the specified mission time.

9. The method for determining spare parts for confined space equipment based on mission success rate according to claim 8, characterized in that: The normal mission execution time of equipment refers to the equipment failure time calculated based on the equipment failure time model.

10. A confined space equipment spare parts determination system oriented towards mission success rate, characterized in that... include: The first module is used to establish an equipment failure time model based on the failure time of replaceable units; The second module is used to perform Monte Carlo simulation on each replaceable component that affects the equipment mission, obtain the failure time of each replaceable component, and then combine it with the equipment failure time model to determine whether the equipment mission is normal or not, and prioritize the replaceable component that first causes the equipment mission to fail to be executed normally to be included in the spare parts list. The third module is used to repeatedly perform Monte Carlo simulation on each replaceable component that affects the equipment mission based on the current spare parts list, to obtain the replaceable components that should be prioritized in the spare parts list, and to determine whether the space occupied by the spare parts has reached the upper limit requirement. The fourth module is used to repeatedly perform Monte Carlo simulation on each replaceable component that affects the equipment mission if the space occupied by the spare parts has not reached the upper limit requirement, to obtain the replaceable components that should be given priority in the spare parts list, until the space occupied by the spare parts reaches the upper limit requirement. If the space occupied by spare parts reaches the upper limit requirement, the output will be the final spare parts list, which includes the types and quantities of equipment spare parts under limited space conditions.

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