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

Abstract

The invention discloses a task success rate-oriented limited space equipment spare part determination method. The method comprises the following steps of: establishing an equipment failure time model based on replaceable unit failure time; performing single simulation, and supplementing a spare part list; and performing iterative simulation, and finally determining a complete spare part list. The method solves the problem that under the premise requirement of a limited space, the supportability of the types and the number of spare parts cannot be reasonably determined.

Description

Technical Field

[0001] The invention belongs to the field of aerospace system design and support engineering technology, and in particular relates to a method for determining spare parts for limited space equipment oriented to mission success rate. Background Art

[0002] In the past, the use scenarios and activity scope of most equipment were relatively limited, and high requirements were not put forward in terms of security resources, especially the storage and transportation of spare parts. Generally speaking, equipment users store spare parts in a fixed place. When a replaceable unit in the equipment is detected to have a fault, the equipment reliability is restored by replacing the spare parts in a timely manner. However, with the diversification of equipment usage scenarios and the continuous expansion of its geographical scope, the original fixed spare parts warehouse guarantee method cannot adapt to the new guarantee needs, and the inability to guarantee spare parts in a timely manner makes it difficult to guarantee the reliability of equipment use. Therefore, some equipment has adopted the guarantee method of mobile spare parts warehouses, which has solved the spare parts supply problem to a certain extent.

[0003] Mobile spare parts warehouses are different from traditional fixed spare parts warehouses, especially in terms of spare parts space. The significant problem brought about by this is how to reasonably determine the types and quantities of spare parts in all replaceable units.

[0004] In traditional fixed-point spare parts support, the spare parts space is large, and all replaceable units can be equipped with a certain number of spare parts, and the specific number is generally determined according to the spare parts satisfaction rate requirements. However, under the premise of limited space, it is impossible to carry all kinds of spare parts, and the number of spare parts calculated according to the spare parts satisfaction rate is large, which may cause the problem that the spare parts space cannot bear it. Summary of the invention

[0005] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art, to provide a method for determining spare parts for limited space equipment oriented to mission success rate, and to solve the problem of planning the types and quantities of spare parts under limited space conditions.

[0006] The object of the present invention is achieved through the following technical solutions: a method for determining spare parts of limited space equipment oriented to 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 unit that affects the equipment mission, obtaining the failure time of each replaceable component unit, and then judging whether the equipment mission is normal or not in combination with the equipment failure time model, and giving priority to the replaceable component unit that first causes the equipment mission to fail to execute normally in the spare parts list; according to the current spare parts list, repeatedly performing Monte Carlo simulation on each replaceable component unit that affects the equipment mission, obtaining the replaceable component unit that is given priority to be included in the spare parts list, and judging whether the space occupied by the spare parts reaches the upper limit requirement; if the space occupied by the spare parts does not reach the upper limit requirement, repeatedly performing Monte Carlo simulation on each replaceable component unit that affects the equipment mission, obtaining the replaceable component unit that is given priority to be included 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, outputting the types and quantities of equipment spare parts under limited space conditions, that is, the final spare parts list.

[0007] The above-mentioned method for determining spare parts for limited space equipment oriented to mission success rate also includes: performing Monte Carlo simulation on the normal mission execution time of the equipment based on the final spare parts list and the equipment failure time model, and calculating and outputting the expected result of the mission success rate.

[0008] In the above-mentioned method for determining spare parts for limited space equipment oriented to mission success rate, the equipment failure time is the failure time when the replaceable component unit fails, resulting in the equipment being unable to perform the task normally.

[0009] In the above-mentioned method for determining spare parts for limited space equipment oriented to mission success rate, the average value of multiple groups of simulated failure times of each replaceable component unit is used as the failure time of each replaceable component unit.

[0010] In the above-mentioned method for determining spare parts for limited space equipment oriented to mission success rate, the number of failure time simulations for each replaceable component unit is the same; and the calculation formula for each group of simulated failure time of each replaceable component unit obeys exponential distribution or Weibull distribution.

[0011] In the above-mentioned limited space equipment spare parts determination method oriented to mission success rate, the replaceable component unit that first causes the equipment mission to fail to be performed normally is the replaceable component unit whose failure time is the same as the equipment failure time calculated according to the equipment failure time model.

[0012] In the above-mentioned limited space equipment spare parts determination method oriented to 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 limited space equipment oriented to mission success rate, the proportion of the time when the equipment normally performs the mission longer than the prescribed mission time is determined as the estimated result of the mission success rate.

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

[0015] A limited space equipment spare parts determination system oriented to task success rate comprises: 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 unit affecting the equipment task, obtaining the failure time of each replaceable component unit, and then judging whether the equipment task is normal or not in combination with the equipment failure time model, and giving priority to the replaceable component unit that first causes the equipment task to fail to be normally executed in the spare parts list; a third module for repeatedly performing Monte Carlo simulation on each replaceable component unit affecting the equipment task according to the current spare parts list, obtaining the replaceable component unit that is given priority to be included in the spare parts list, and judging whether the space occupied by the spare parts reaches the upper limit requirement; a fourth module for repeatedly performing Monte Carlo simulation on each replaceable component unit affecting the equipment task, obtaining the replaceable component unit that is given priority to be included 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 does not reach the upper limit requirement; if the space occupied by the spare parts reaches the upper limit requirement, outputting the types and quantities of the equipment spare parts under limited space conditions, i.e., the final determined spare parts list.

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

[0017] (1) The present invention can solve the problem of being unable to reasonably determine the types and quantities of spare parts under the premise of limited space, effectively provide a limited space equipment spare parts determination method oriented to the mission success rate, and realize the mission reliability prediction under the spare parts list;

[0018] (2) The present invention establishes a limited space equipment spare parts determination process oriented to the mission success rate. The spare parts list is gradually determined through multiple iterative simulations, solving the problem of how to determine the type and quantity of spare parts in a limited space;

[0019] (3) The present invention constructs an equipment failure time model and establishes the relationship between equipment failure time and replaceable unit failure time, which lays a foundation for determining replaceable units that fail first and realizing the prediction of equipment mission success rate;

[0020] (4) The present invention develops a method for predicting the failure time of replaceable units. The Monte Carlo simulation method is used in combination with the task reliability block diagram to solve the problem that the reliability level of complex models cannot be theoretically calculated, and the prediction of unit failure time is realized when the parallel model (hot standby) and the bypass model (cold standby) coexist. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0022] Figure 1 It is a schematic diagram of a limited space equipment spare parts determination process oriented to mission success rate provided by an embodiment of the present invention;

[0023] Figure 2 It is a reliability block diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

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

[0026] Establishing an equipment failure time model based on the failure time of replaceable units; wherein, determining the replaceable component units that affect the equipment mission, and establishing an equipment failure time model based on the failure time of replaceable units according to the logical relationship between the normal equipment mission and the replaceable component units;

[0027] Conduct Monte Carlo simulation on each replaceable component unit that affects the equipment mission, obtain the failure time of each replaceable component unit, and then use the equipment failure time model to determine whether the equipment mission is normal or not. The replaceable component unit that first causes the equipment mission to fail to be performed normally is included in the spare parts list first;

[0028] According to the current spare parts list, Monte Carlo simulation is repeatedly performed on each replaceable component unit that affects the equipment mission, and the replaceable component units that are prioritized in the spare parts list are obtained to determine whether the space occupied by the spare parts reaches the upper limit requirement;

[0029] If the space occupied by spare parts does not reach the upper limit requirement, Monte Carlo simulation is repeated for each replaceable component unit that affects the equipment mission to obtain the replaceable component units that are prioritized 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 types and quantities of equipment spare parts under limited space conditions are output, that is, the final spare parts list.

[0030] The method further includes: performing a Monte Carlo simulation on the normal mission execution time of the equipment according to the finalized spare parts list and the equipment failure time model, and calculating and outputting the predicted result of the mission success rate. The predicted result of the mission success rate is determined as the proportion of the normal mission execution time of the equipment that is greater than the specified mission time. The normal mission execution time of the equipment refers to the equipment failure time calculated according to the equipment failure time model.

[0031] The equipment failure time refers to the failure time when a replaceable component unit fails, causing the equipment to be unable to perform tasks normally.

[0032] The failure time of each component unit includes: taking the average value of multiple groups of simulation failure times of a certain component unit as the failure time of the component unit.

[0033] When the average value of multiple groups of simulated failure times of a component unit is used as the failure time of the component unit, the number of failure time simulations for each component unit is the same; the calculation formula for each group of simulated failure times of each component unit is determined according to the distribution type obeyed by the product, generally obeying exponential distribution or Weibull distribution, etc.

[0034] The component unit that first causes the equipment mission to fail to be performed normally refers to the component unit whose failure time is the same as the equipment failure time calculated according to the equipment failure time model.

[0035] The space occupied by spare parts refers to the sum of the number of all types of spare parts in the currently determined spare parts list multiplied by the space occupied by a single spare part.

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

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

[0038] When evaluating mission reliability, a mission reliability model must be established, which generally includes a mission reliability block diagram and mathematical expressions. Based on the mission reliability block diagram, we can clearly give the logical connection between the success or failure of the mission and whether the functions of each component unit are normal, and further obtain a mathematical model between the time the equipment can normally perform the mission and the failure time of each replaceable unit.

[0039] For example, in the series model, the time that the equipment can normally perform the task is:

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

[0041] In the formula,

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

[0043] T i —failure time of the ith series unit;

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

[0045] Similarly, in the parallel model, the time that the equipment can normally perform the task is:

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

[0047] In the formula,

[0048] T i —failure time of the ith parallel unit;

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

[0050] Other reliability models (such as k / n model, collateral model, etc.) can 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 equipment is predicted through the failure time simulation of replaceable units. The key is to locate the "short board" that causes the equipment failure, 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 failure time prediction result is related to its mean time between failures (MTBF), and the simulation relationship is:

[0054] T=-MTBF×ln(rand)

[0055] In the formula,

[0056] T—random failure time;

[0057] rand—A random number between 0 and 1.

[0058] Since the failure time has a certain degree of randomness, by generating multiple sets of random numbers, the statistical value of the random failure time is obtained, which is the expected result of the failure time. It should be noted that in order 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, the failure time of each unit is simulated and predicted, and a set of failure times (T1, T2, T3, ..., T n ), according to the failure time expression of the series model listed in the first step, its failure time is the minimum value of the n T values. This method is also applicable to parallel models, n / k models, etc. It is worth noting that the failure time of a simple model can be obtained through theoretical calculations, such as the failure time of a single-point link is the MTBF value; the failure time of a dual redundant link is 1.5 times the MTBF value of a single unit. However, for complex models, especially in the case of superposition of redundancy and cold standby, it is impossible to obtain an accurate failure time through theoretical calculations, and it is necessary to use the simulation method proposed in this article to obtain the expected results of the failure time.

[0060] After the failure time of all replaceable units is estimated, the equipment failure time can be obtained according to the failure time model in the first step, and the replaceable unit that fails first and causes the equipment to be unable to complete the task normally can be further determined. In order to extend the time for the equipment to complete the task, it needs to be included in the spare parts list, which completes the replenishment process of the single spare parts list.

[0061] (3) Iterate the simulation to finally determine the complete spare parts list.

[0062] When the spare parts list changes, the equipment failure time model will also change. The more spare parts a replaceable unit has, the longer the failure time of the unit 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 re-identify the "shortcomings" in the current situation to determine the types of replaceable units that need to be added to the spare parts list.

[0063] Repeated simulations are conducted to identify the failure time short board, and the spare parts list is continuously supplemented until the space occupied by the spare parts reaches the upper limit and no more spare parts can be added. This is the effective space equipment spare parts list for mission success rate.

[0064] (4) Achieve the expected success rate of the task within the specified time

[0065] The Monte Carlo method is still used to estimate the ability of the equipment to complete the task under the current spare parts list. In a single simulation, the single random failure time of the replaceable unit (including spare parts) is first obtained according to the random number, and the equipment failure time in this simulation is calculated according to the equipment random failure time model, and this time is compared with the specified task time. If the equipment failure time is greater than the specified task time, the equipment can complete the task in this simulation; otherwise, it is recorded as unable to complete the task this time.

[0066] After multiple simulations, the number of times the equipment can complete the task is counted, and the proportion of these times is the estimated result of the equipment task success rate.

[0067] Example:

[0068] The present invention is described in detail below through specific examples.

[0069] (1) Taking a series-parallel model as an example, the application of the method of the present invention is described. Figure 2 As shown in the figure, there are 5 types of units, of which unit 1 and unit 3 are single points, unit 2 and unit 4 are dual redundant, and unit 5 is triple redundant. Under this model, the time for the equipment to perform tasks normally is:

[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 serial node, T s2 =max(T 2-1 ,T 2-2 ), is the longer failure time of the two units 2;

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

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

[0076] T s5 —Failure time of the fifth serial node, T s5 =max(T 5-1 ,T 5-2 , T 5-3 ), which is the longer failure time among the 3 units 5.

[0077] (2) Assume that the product information of 5 types of replaceable units is as shown in the following table.

[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] The number of random number generation in the Monte Carlo simulation is taken as 5000, and the failure time of unit 1 is estimated to be T according to the MTBF value of each unit. s1 =4998.8h, the estimated failure time of unit 2 under dual redundancy is T s2 =4508.4h, the estimated failure time of unit 3 is T s3 =6512.1h, the expected failure time of unit 4 under dual redundancy is T s4 =6746.2h, the estimated failure time of unit 5 under triple redundancy is T s5 =5506.4h. The time during which the equipment can normally perform its tasks is:

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

[0082] It can be concluded that unit 2 has the shortest failure time and is the first "shortcoming" that causes the equipment to fail to complete the task normally. Therefore, unit 2 is 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 failure time prediction values ​​can be re-predicted using the last simulation results for unit 2.

[0084] Unit 2 is changed from the previous dual-redundancy model to a dual-redundancy, one cold standby model. Under the new working conditions, two unit 2 products work simultaneously. When one of them fails, the spare part can be replaced to take over the work. When both the spare part taking over and the other product fail, the unit 2 link fails completely, thus causing the equipment failure. According to the above logic, the Monte Carlo method is used to obtain the estimated failure time of unit 2 as T s2 =6024.6h. At this time, the time for the equipment to perform tasks normally is:

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

[0086] Therefore, it can be concluded that unit 1 has the shortest failure time and is the first "shortcoming" that causes the equipment to fail to complete the task 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 spaces.

[0087] The above process is repeated until the spare parts space reaches the upper limit (assuming it is 50), and the spare parts list under limited space is obtained. The spare parts list change process is shown in the following table.

[0088] Table 2 Spare parts list change process

[0089]

[0090]

[0091] Finally, the number of spare parts for the five replaceable units is determined to be: 1 each for unit 1, unit 3, and unit 4, and 2 each for unit 2 and unit 5. The occupied spare parts space reaches the upper limit, which is exactly 50.

[0092] (4) The number of spare parts for the five replaceable units has been determined. When the mission time is 2000 h, after 10,000 simulations, the number of times the equipment can complete the mission is 8376. Therefore, the estimated result of the equipment mission success rate within the specified time is 0.8376.

[0093] The present embodiment also provides a limited space equipment spare parts determination system oriented to mission success rate, the system comprising: a first module, used to establish an equipment failure time model based on the failure time of replaceable units; a second module, used to perform Monte Carlo simulation on each replaceable component unit that affects the equipment mission, obtain the failure time of each replaceable component unit, and then judge whether the equipment mission is normal or not in combination with the equipment failure time model, and give priority to the replaceable component unit that first causes the equipment mission to fail to execute normally in the spare parts list; a third module, used to repeat the Monte Carlo simulation on each replaceable component unit that affects the equipment mission according to the current spare parts list, obtain the replaceable component unit that is given priority to be included in the spare parts list, and judge whether the space occupied by the spare parts meets the upper limit requirement; a fourth module, used to repeat the Monte Carlo simulation on each replaceable component unit that affects the equipment mission if the space occupied by the spare parts does not meet the upper limit requirement, obtain the replaceable component unit that is given priority to be included in the spare parts list, until the space occupied by the spare parts meets the upper limit requirement; if the space occupied by the spare parts meets the upper limit requirement, then output the types and quantities of equipment spare parts under limited space conditions, that is, the final determined spare parts list.

[0094] This embodiment can solve the problem of the inability to reasonably determine the type and quantity of spare parts under the premise of limited space, effectively provide a limited space equipment spare parts determination method oriented to the task success rate, and realize the task reliability prediction under the spare parts list; this embodiment establishes a limited space equipment spare parts determination process oriented to the task success rate. The spare parts list is gradually determined by multiple iterative simulations, which solves the problem of how to determine the type and quantity of spare parts in a limited space; this embodiment constructs an equipment failure time model, establishes the connection between the equipment failure time and the failure time of the replaceable unit, and lays the foundation for determining the replaceable units that fail first and realizing the equipment task success rate prediction; this embodiment develops a method for predicting the failure time of replaceable units. The Monte Carlo simulation method is used, combined with the task reliability block diagram, to solve the problem that complex models cannot theoretically calculate the reliability level, and realize the unit failure time prediction when the parallel model (hot standby) and the bypass model (cold standby) coexist.

[0095] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for determining spare parts for limited space equipment oriented to mission success rate, characterized in that include: Establish equipment failure time model based on replaceable unit failure time; Conduct Monte Carlo simulation on each replaceable component unit that affects the equipment mission, obtain the failure time of each replaceable component unit, and then use the equipment failure time model to determine whether the equipment mission is normal or not. The replaceable component unit that first causes the equipment mission to fail to be performed normally is included in the spare parts list first; According to the current spare parts list, Monte Carlo simulation is repeatedly performed on each replaceable component unit that affects the equipment mission, and the replaceable component units that are prioritized in the spare parts list are obtained to determine whether the space occupied by the spare parts reaches the upper limit requirement; If the space occupied by the spare parts does not reach the upper limit requirement, the Monte Carlo simulation is repeated for each replaceable component unit that affects the equipment mission to obtain the replaceable component units that are 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 types and quantities of equipment spare parts under limited space conditions will be output, that is, the final spare parts list.

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

3. The method for determining spare parts for limited space equipment based on mission success rate according to claim 1, characterized in that: The equipment failure time refers to the failure time when the replaceable component unit fails, resulting in the equipment being unable to perform tasks normally.

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

5. The method for determining spare parts for limited space equipment oriented to mission success rate according to claim 4 is characterized in that: The number of failure time simulations for each replaceable component unit is the same; and the calculation formula for each group of simulated failure time of each replaceable component unit obeys exponential distribution or Weibull distribution.

6. The method for determining spare parts for limited space equipment oriented to mission success rate according to claim 1, characterized in that: The replaceable component unit that first causes the equipment mission to fail to be performed normally is the replaceable component unit whose failure time is the same as the equipment failure time calculated according to the equipment failure time model.

7. The method for determining spare parts for limited space equipment oriented to mission success rate according to claim 1, characterized in that: The space occupied by spare parts is the sum of the number of all types of spare parts in the currently determined spare parts list multiplied by the space occupied by a single spare part.

8. The method for determining spare parts for limited space equipment oriented to mission success rate according to claim 2 is characterized in that: The estimated mission success rate is determined by the proportion of the equipment's normal mission execution time that is greater than the prescribed mission time.

9. The method for determining spare parts for limited space equipment oriented to 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 limited space equipment spare parts determination system oriented to mission success rate, characterized by 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 unit that affects the equipment mission, obtain the failure time of each replaceable component unit, and then judge whether the equipment mission is normal or not in combination with the equipment failure time model, and give priority to the replaceable component unit that first causes the equipment mission to fail to be performed normally and put it into the spare parts list; The third module is used to repeatedly perform Monte Carlo simulation on each replaceable component unit that affects the equipment mission according to the current spare parts list, obtain the replaceable component units that are preferentially included in the spare parts list, and determine whether the space occupied by the spare parts reaches the upper limit requirement; The fourth module is used to repeatedly perform Monte Carlo simulation on each replaceable component unit that affects the equipment mission if the space occupied by the spare parts does not reach the upper limit requirement, so as to obtain the replaceable component units that are preferentially included 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 types and quantities of equipment spare parts under limited space conditions will be output, that is, the final spare parts list.

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