Rapid turnover influence factor identification method based on full-profile task analysis

Abstract

The invention discloses a rapid turnover influence factor identification method based on full-profile task analysis. The method comprises the following steps: analyzing a system task profile and a reuse condition; analyzing typical characteristics of system reliability; modeling average task reliability of a system single-machine product; reliability modeling of a single task of the system; reliability modeling of a system full-life-cycle reuse task is carried out; analyzing the whole-life-cycle average task reliability of the system; analyzing task reliability weak links in the full-task process of the system; and rapid turnover influence factor analysis of full-profile task analysis. According to the method disclosed by the invention, the process of determining the rapid turnover influence factors of the full-profile task is standardized, and the method has reference significance for determining the rapid turnover influence factors of the full-profile task under the condition that other types of reusable carrier rocket systems and reusable suborbital aircraft systems are reused.

Description

Technical Field

[0001] The present invention belongs to the technical field of system reliability, and in particular relates to a method for identifying factors affecting rapid turnover based on full-profile mission analysis. Background Art

[0002] The development and utilization of reusable suborbital vehicles provides an effective way for humanity to conduct low-cost, high-efficiency space transportation activities. The high reliability, safety, and affordability of reusable suborbital vehicles are a prerequisite for the in-depth development and utilization of space resources. Safe, reliable, efficient, and economical reusable suborbital vehicles can effectively promote the development of future commercial space transportation systems.

[0003] Reusable suborbital vehicles experience unique and complex environments during their operation. Assessing reusability with maintenance support is a key consideration in reusable suborbital vehicle system design. During a suborbital vehicle's mission, there is a critical relationship between system reliability and rapid turnaround capability. The reliability of each layer of the system directly impacts rapid turnaround capability.

[0004] The advent of the space shuttle has made reusable spacecraft a reality. With the rise of reuse-related technology research and applications in the aerospace field, how to conduct a quantitative reliability assessment of reusable suborbital vehicle systems has become an urgent issue to be addressed, especially the evaluation of the reusability of reusable suborbital vehicle systems.

[0005] Reusable suborbital spacecraft systems are categorized into two types: autonomous flight and launch into orbit as a payload using a launch vehicle system. Reusable manned spacecraft are launch vehicle payloads, while systems like Starship or suborbital spacecraft operate autonomously. Reusable manned spacecraft mission reliability involves both launch reliability and flight reliability, while systems like Starship or suborbital spacecraft can employ a single mission reliability metric encompassing both. However, due to the challenges of assessing reusable mission reliability, there is currently no effective method for assessing the full lifecycle mission reliability of reusable suborbital spacecraft systems.

[0006] In summary, the traditional reliability system assessment method cannot meet the needs of dynamic quantitative analysis of the reliability of reusable suborbital spacecraft systems, nor can it characterize the weak links that affect the system mission reliability at different stages and times of the reusable suborbital spacecraft system with maintenance support. Summary of the Invention

[0007] The technology of the present invention solves the problem: Overcoming the shortcomings of the existing technology, providing a method for identifying factors affecting rapid turnover based on full-profile mission analysis, aiming to solve the problem of dynamic quantitative evaluation of the reliability of reusable suborbital vehicle systems and identification of weak links in system mission reliability under maintenance support.

[0008] In order to solve the above technical problems, the present invention discloses a method for identifying factors affecting rapid turnover based on full-profile task analysis, comprising:

[0009] Analyze and determine the mission profile and reuse conditions of reusable suborbital spacecraft systems;

[0010] Analyze and determine the typical reliability characteristics of reusable suborbital spacecraft systems;

[0011] Conduct average mission reliability modeling for single-unit products of reusable suborbital spacecraft systems;

[0012] Conduct single mission reliability modeling of reusable suborbital spacecraft systems;

[0013] Conducting reusable mission reliability modeling for the full life cycle of reusable suborbital spacecraft systems;

[0014] Analyze and determine the average mission reliability of the reusable suborbital vehicle system throughout its life cycle;

[0015] Analyze and identify weaknesses in mission reliability during the entire mission of the reusable suborbital vehicle system;

[0016] Analyze and determine the factors that influence the rapid turnaround of full-profile mission analysis.

[0017] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, the mission profile and reuse conditions of the reusable suborbital vehicle system are analyzed and determined, including:

[0018] According to the flight process of the reusable suborbital vehicle, the mission profile of the reusable suborbital vehicle system is divided into: take-off phase, ascent phase, orbit insertion phase, on-orbit flight phase, deorbit phase and unpowered return phase;

[0019] Conduct single mission profile analysis; the single mission profile analysis considers the implementation of each subtask process of a typical single mission of the system and conducts the analysis work according to the process-based analysis method;

[0020] Conduct repeated mission profile analysis, including: system composition analysis, clarify the factors affecting reliability during the system mission process: human factors, software and hardware equipment factors, and environmental factors; and provide multiple mission profile process diagrams.

[0021] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, typical reliability characteristics of reusable suborbital vehicle systems are analyzed and determined, including:

[0022] Analysis of incomplete system reuse characteristics, including analysis of fully reusable products, partially reusable products, and single component reusability; combining the analysis results of fully reusable products, partially reusable products, and single component reusability to identify reusable subsystems, key subsystems, and related stand-alone products;

[0023] Analysis of incomplete reuse characteristics of subsystems, including: Based on the identified reuse subsystems, key subsystems, and related stand-alone products, clarify the reuse characteristics of the subsystem throughout the entire mission cycle, including the number of reuses and the usage cycle time;

[0024] Analysis of the lifespan and reliability characteristics of single-unit products;

[0025] The maintainability analysis of individual products includes: allocating system maintainability indicators based on the rapid turnover cycle requirements of reusable suborbital vehicle systems, further clarifying the requirements related to the maintainability of individual products through the indicator allocation, and determining the results of the maintainability analysis of individual products; based on the results of the maintainability analysis of individual products, providing the maintenance timing for products at each level and determining the maintenance classification of individual products: condition-based maintenance, planned / preventive maintenance, and corrective maintenance.

[0026] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, the average mission reliability modeling of a single-unit product of a reusable suborbital vehicle system includes: exponential distribution type average mission reliability modeling of reusable products and Weibull distribution type average mission reliability modeling of reusable products.

[0027] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile task analysis,

[0028] The modeling process of average mission reliability of exponentially distributed reusable products is as follows:

[0029] Determine the average mission reliability evaluation model R of exponentially distributed reusable products + (t + ):

[0030]

[0031] Where λ represents the failure rate of exponentially distributed reused products, t + Represents the exponentially distributed repeated use of product task time;

[0032] According to the following formula (2), the point estimate of the failure rate of exponentially distributed reused products is calculated:

[0033]

[0034] Among them, r + represents the exponentially distributed failure rate of repeated-use products, T + Indicates the total cumulative test time of exponentially distributed reusable products;

[0035] According to the following formula (3), the upper limit of the failure rate of exponentially distributed reused products λ is calculated U :

[0036]

[0037] According to the following formula (4), the estimated reliability value of the exponential distribution type reused product is calculated:

[0038]

[0039] According to the following formula (5), the lower limit of the reliability of the exponential distribution type reused product R is calculated. L :

[0040]

[0041] Then, the normalized reliability function cumulative probability distribution function of exponentially distributed reused products is recorded as

[0042]

[0043] in, represents the starting time of the exponentially distributed repeated use product task, Indicates the end time of the exponentially distributed repeated use product task;

[0044] The modeling process of the average mission reliability of Weibull distribution reused products is as follows:

[0045] Determine the average mission reliability evaluation model R of Weibull distribution reused products - (t - ):

[0046]

[0047] Where η represents the characteristic life point of the Weibull distribution type repeated use product, m represents the shape parameter, t - represents the failure time of a Weibull-distributed repeated-use product;

[0048] According to the following formula (8), the estimated failure time of the Weibull distribution repeated use product is calculated

[0049]

[0050] Among them, n - The number of products representing the Weibull-distributed repeated use of the product in the test and actual use, represents the pth Weibull distribution repeated use product failure time;

[0051] According to the following formula (9), the point estimate of the characteristic life of the Weibull distribution reused product is calculated:

[0052]

[0053] Among them, r - represents the number of Weibull-distributed repeated-use product failures;

[0054] According to the following formula (10), the estimated reliability value of the Weibull distribution reused product is calculated as

[0055]

[0056] According to the following formula (11), the lower limit of the reliability of the Weibull distribution reused product is calculated

[0057]

[0058] Then, the normalized reliability function cumulative probability distribution function of the Weibull distribution type reused product is recorded as

[0059]

[0060] in, represents the starting time of the Weibull distribution repeated use product task, represents the end time of the Weibull distribution repeated use product task, express The point estimate of the characteristic life span at a given moment, express Point estimate of characteristic lifetime at a moment.

[0061] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, single-mission reliability modeling of the reusable suborbital vehicle system is carried out, including:

[0062] Build reliability models and collect reliability data for stand-alone products;

[0063] The deconstruction and synthesis of the system reliability model include: for the functions that the system needs to perform in each subtask in a single task, clarify the key subsystems and key products involved in the implementation stage of each subtask, and clarify the redundant backup relationship of products at each level; according to the specific situation of each subtask, select the corresponding modeling method to construct the subtask reliability model; according to the profile of the system executing a single task, use the event tree to model the task process, and integrate the reliability models of each subtask into the event tree model to form a comprehensive model of single task reliability.

[0064] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, the reliability modeling of the reusable suborbital vehicle system's full life cycle reusable mission is carried out, including:

[0065] The following assumptions are made: long-life products will not fail during the system's life cycle; limited-life products will be replaced regularly during the system's life cycle;

[0066] Based on the given assumptions, process-based task modeling, function-based fault modeling, and process-based maintenance modeling are carried out.

[0067] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, when analyzing and determining the average mission reliability of the reusable suborbital vehicle system throughout its life cycle, the system mission reliability probability quantification value is defined as R ys :

[0068]

[0069] in, represents the function related to the reliability of the kth subsystem, k = 1, 2, ..., K, K represents the number of subsystems in the system; α, β, γ are abstract parameters related to the reliability of the subsystem; Indicates the time t between the kth subsystem and the system's full life cycle s and other functions related to time-related parameter variables t′; t is and t ie It represents the start and end time of the system executing a single flight mission, and i represents the number of times the system repeats the mission.

[0070] In the above-mentioned method for identifying factors affecting rapid turnover based on full-profile mission analysis, the weak links in mission reliability of the reusable suborbital vehicle system throughout the entire mission process are analyzed and identified, including:

[0071] Based on the single mission reliability modeling of the reusable suborbital spacecraft system, the reusable mission reliability modeling of the reusable suborbital spacecraft system throughout its life cycle, and the average mission reliability of the reusable suborbital spacecraft system throughout its life cycle, a mission reliability change trend diagram of the reusable suborbital spacecraft system throughout its mission process is obtained through simulation analysis;

[0072] Based on the mission reliability change trend diagram of the reusable suborbital vehicle system during the entire mission process obtained from the simulation analysis, the upper and lower bounds of the mission reliability probability quantification "function" during the entire mission process, the system reliability weak interval and interval extremes, and the reliability weak links during the system's mission execution are determined to clarify the reliability weak links of the system during the entire mission process.

[0073] In the above-mentioned method for identifying factors influencing rapid turnover based on full-profile task analysis, the factors influencing rapid turnover of full-profile task analysis are analyzed and determined, including:

[0074] According to the clear reliability weak links of the system throughout the entire mission process and the characteristics of their impact on the system's rapid turnover capability during mission execution, the rapid turnover influencing factors corresponding to the reliability weak links are divided into: direct influencing factors, indirect influencing factors, and main influencing factors.

[0075] The present invention has the following advantages:

[0076] (1) The present invention discloses a method for identifying factors affecting rapid turnover based on full-profile mission analysis, which can more effectively reflect the dynamic characteristics of system reliability, and can obtain the possibility and uncertainty of different system states occurring during the entire mission cycle. At the same time, it can identify the weak links of system tasks, so as to comprehensively reflect the factors affecting the reliability of system tasks and the key subtasks affecting the success of tasks at different times during the entire life cycle of the system. Through feedback design, the reliability of the mission process is improved, and at the same time, a quantitative analysis basis is provided for improving or enhancing system design, mission planning, etc., which can provide support and decision-making for system reliability analysis and design.

[0077] (2) The present invention discloses a method for identifying factors affecting rapid turnover based on full-profile mission analysis, which can meet the work requirements of comprehensive modeling and dynamic simulation evaluation of the full life cycle mission process of typical reusable suborbital vehicle systems, reusable launch vehicle systems and other spacecraft involving reusable missions, and has good prospects for promotion and application.

[0078] (3) The present invention discloses a method for identifying factors affecting rapid turnover based on full-profile mission analysis, taking into account the mission reliability evaluation of reusable stand-alone products, and innovatively proposes a quantitative calculation formula for average mission reliability and a modeling and analysis method for system mission reliability under the conditions of full life cycle reuse of reusable suborbital spacecraft systems. It provides a specific implementation plan for the modeling of a comprehensive integrated model for mission process reliability evaluation of complex reusable suborbital spacecraft systems, and also provides a specific method flow and calculation formula for the softwareization of subsequent modeling and simulation technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 1 is a flow chart of a method for identifying factors affecting rapid turnover based on full-profile task analysis in an embodiment of the present invention;

[0080] Figure 2 This is a schematic diagram of a repeated (multiple) task profile analysis in an embodiment of the present invention;

[0081] Figure 3 This is a schematic diagram of a typical single-mission process model for the entire life cycle of a system according to an embodiment of the present invention;

[0082] Figure 4 2 is a schematic diagram of a system reliability evaluation result in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments disclosed in the present invention will be described in further detail below with reference to the accompanying drawings.

[0084] The core idea of ​​this invention is to disclose a method for identifying factors affecting rapid turnover based on full-profile task analysis, which mainly includes the following eight parts:

[0085] (1) Analyze and determine the mission profile and reuse status of the reusable suborbital vehicle system. This section needs to clearly define the mission profile of the reusable suborbital vehicle system, including the single-use mission profile and the reuse (multiple) mission profile; based on the mission profile analysis, clarify the composition of the vehicle system and the reuse characteristics and classification of products at all levels, and clarify long-life products, limited-life products, and the maintainability of products.

[0086] (2) Analyze and determine the typical reliability characteristics of reusable suborbital spacecraft systems. This section requires analyzing the typical reliability characteristics of products under reuse conditions based on the reuse analysis of products at all levels of the spacecraft system, and clarifying the types of reliability characteristics of products at all levels.

[0087] (3) Model the average mission reliability of a single product of a reusable suborbital vehicle system. After determining the method for constructing a product reliability assessment model based on the reuse characteristics of a single product, a dynamic trend chart of the average mission reliability probability quantification value (mission reliability) of a reusable single product over time can be obtained through calculation and analysis. For reusable single products, in order to obtain a dynamic trend chart of mission reliability over time, it is necessary to effectively collect product life and reliability data, specifically including statistics on failure and non-failure data of products that have reached their service life. For individual products (parts), their reliability characteristics vary. Based on the analysis and identification of the main failure modes and failure mechanisms of the products, the reliability characteristic quantities of the products should be determined and the product reliability assessment model should be clarified. Generally, a unit reliability assessment model should be established based on the mission profiles and different environmental conditions, mission functions and time requirements experienced during the actual mission process, and according to the main failure modes of the product during the use phase (within the life cycle).

[0088] (4) Conduct single mission reliability modeling for the reusable suborbital vehicle system. Based on the system's single mission mission profile, a comprehensive model for system single mission reliability assessment is constructed using the PRA method.

[0089] (V) Conducting full-life cycle reusable mission reliability modeling for reusable suborbital vehicle systems. Combining a comprehensive model for single-mission reliability assessment with the reuse characteristics of products at all levels of the system and a single-machine product cluster (here, a single-machine product cluster refers to a collection of single-machine products with repairable products that perform full-life cycle missions of the system under the assumption of planned maintenance), conduct full-life cycle mission reliability modeling for reusable suborbital vehicle systems. Since the number of missions performed and mission modes may vary throughout the system's life cycle, the full-life cycle reusable mission reliability model for reusable suborbital vehicle systems can be formulated in the form of piecewise functions. When conducting simulation modeling, it is necessary to construct reliability models for different missions throughout the life cycle based on the system's composition, etc.

[0090] (VI) Analyze and determine the average mission reliability of the reusable suborbital vehicle system over its entire lifecycle. This section involves determining the reliability of the reusable suborbital vehicle system in performing each mission. Furthermore, it is necessary to determine the system's reliability in completing a single mission at different points in time during each mission. By determining the system's reliability in performing each mission and the reliability of completing a single mission at different points in time, a mission reliability trend curve can be drawn over the reusable suborbital vehicle system's entire lifecycle.

[0091] (7) Analyze and identify weaknesses in the mission reliability of the reusable suborbital vehicle system throughout its entire mission. Draw a mission reliability trend curve for the reusable suborbital vehicle system throughout its lifecycle. Further, based on the required values ​​of the system's mission reliability indicators throughout its entire mission, determine the system's reliability weaknesses and range extremes. Based on these weaknesses and range extremes, identify weaknesses in the system's reliability during mission execution.

[0092] (8) Analyze and determine the factors that affect the rapid turnaround of full-profile mission analysis. Based on the analysis of the weak links in mission reliability during the full mission of the reusable suborbital vehicle system, analyze and determine the factors that affect the rapid turnaround of full-profile mission analysis.

[0093] Reference Figure 1 In this embodiment, the method for identifying factors affecting rapid turnover based on full-profile task analysis specifically includes:

[0094] Step 1: Analyze and determine the mission profile and reuse status of the reusable suborbital vehicle system.

[0095] In this embodiment, the specific implementation process of step 1 is as follows:

[0096] (11) According to the flight process of the reusable suborbital vehicle, the mission profile of the reusable suborbital vehicle system is divided into: take-off phase, ascent phase, orbit entry phase, on-orbit flight phase, deorbit phase and unpowered return phase.

[0097] (12) Conduct single mission profile analysis.

[0098] The single-task profile analysis mainly considers the implementation of each subtask process of a typical single task of the system, and conducts the analysis work according to the process-based analysis method, specifically involving the analysis of the task process, subtask process and subtask implementation involving the system, subsystem and single-machine product. The initial cause event, intermediate event and result status of the single-task event tree modeling are clarified through analysis. On the basis of the event tree modeling, the fault tree (FT), reliability block diagram (RBD) and other models are constructed for each intermediate event. For specific methods, please refer to the relevant method flow of GB / T29075.

[0099] (13) Repeated (multiple) mission profile analysis

[0100] Repeated (multiple) mission profile analysis mainly includes: system composition analysis, clarifying the factors affecting the reliability of the system mission process: human factors, hardware and software equipment factors and environmental factors; providing multiple mission profile process diagrams, such as Figure 2 shown.

[0101] Based on the analysis of single-mission profiles, the system's reuse mission requirements are further considered. Key performance metrics for reuse missions are set based on the requirements analysis, and the key technical performance indicators (TPMs) of the system under full-lifecycle mission conditions are given—the base number of full-mission cycle reliability indicators (defined as the reliability of the system completing the mission within the full mission cycle being no less than a specified value). Based on the multi-mission profile process diagrams obtained through analysis, the reliability modeling of a single typical mission process is used as the baseline model. Based on the reuse capabilities (number of reuses and maintenance information) of the system, subsystem, and key units, the presence of dynamic feature units in the baseline model is clarified. Specifically, these include: a) the number of times a reused product can be reused; b) information on the maintenance and replacement of reused products (condition-based maintenance, planned maintenance, preventive maintenance, etc.); and c) the lifespan and reliability requirements of reused products.

[0102] Step 2: Analyze and determine the typical reliability characteristics of the reusable suborbital spacecraft system.

[0103] Different from traditional disposable spacecraft, the present invention proposes to analyze the typical reliability characteristics of each level of reusable suborbital spacecraft system from the following aspects, and clarify the relevant parameters corresponding to the characteristics through characteristic analysis. According to the reusability of the system, subsystem, key single machine, etc. determined by the repeated (multiple) mission profile analysis in sub-step (13) of step 1, the typical characteristics of system reliability are analyzed. Specifically including:

[0104] (21) The system does not fully reuse feature analysis.

[0105] Due to economic and system structural constraints, current spacecraft systems cannot achieve 100% reuse. This necessitates maintenance, testing, and equipment replacement between missions. These maintenance and replacements make the composition of spacecraft systems inherently dynamic, and the actual components of the system may change dynamically between missions.

[0106] According to the reuse characteristics of spacecraft systems, the following classifications can be made:

[0107] Fully reusable product analysis: The entire spacecraft system is reusable, such as the starship system is reusable.

[0108] Analysis of partially reusable products: Parts of the entire spacecraft system are reusable, such as the first stage / booster are reusable.

[0109] Analysis of single component reusability: It is relatively rare that only a single component product of the entire spacecraft system has the ability to be reused.

[0110] Combining the above three types of analysis results, reusable subsystems, key subsystems and related stand-alone products are identified.

[0111] (22) Analysis of incomplete reuse characteristics of subsystems.

[0112] Based on the identified reusable subsystems, key subsystems and related stand-alone products, the reusable characteristics of the subsystem throughout the entire mission cycle are clarified, including parameters such as the number of reuses, usage cycle time (typical single mission), etc.

[0113] (23) Analysis of the lifespan and reliability characteristics of individual products.

[0114] For reusable suborbital vehicle systems, their mission-completion capabilities are related to two factors: long-life products and limited-life products. The reliability of long-life products generally declines with increasing reuse. However, overall, the reliability of long-life products remains high throughout their life cycle. The failure and safety risks of long-life products are relatively low, and the product remains "intrinsically safe."

[0115] For life-limited products, their reusability is relatively short, and their replacement needs to be determined based on their performance throughout the life cycle of the reusable suborbital spacecraft system. For life-limited products currently in use, their reliability deteriorates with increasing use, necessitating timely replacement. Once a life-limited product is replaced, the spacecraft system's reliability level will experience a brief improvement, and this improvement will fluctuate periodically with the periodic replacement of the life-limited product.

[0116] (24) Maintainability analysis of single-unit products.

[0117] Based on the rapid turnover cycle requirements of the reusable suborbital spacecraft system, system maintainability indicators are allocated. Through the indicator allocation, the relevant requirements for the maintainability of individual products are further clarified, and the maintainability analysis results of individual products are determined. Based on the maintainability analysis results of individual products, the maintenance timing of each level of products is given, and the maintenance classification of individual products is determined: condition-based maintenance, planned / preventive maintenance, and corrective maintenance.

[0118] Step 3: Model the average mission reliability of a single product of the reusable suborbital vehicle system.

[0119] In this embodiment, the average mission reliability modeling of a single-machine product can be carried out based on the typical reliability characteristics of the reusable products at each level of the system obtained in step 2.

[0120] For stand-alone system products, there are cases where long-life products perform a given function multiple times, and there are also cases where multiple products perform a given function multiple times. Therefore, it is necessary to examine the reliability level of a single mission during the full life cycle of a reusable suborbital vehicle system. Since the reliability level of a single mission during the full life cycle of a reusable suborbital vehicle system will be determined by examining the average mission reliability level of the reusable suborbital vehicle system's stand-alone products, a stand-alone product cluster can be identified (a stand-alone product cluster is defined as a collection of one or more identical or functionally similar stand-alone products that perform the system's specified functions during the full life cycle of the reusable suborbital vehicle system). The system's full life cycle stand-alone product cluster can reflect the number and frequency of repairs and replacements for a single stand-alone product during the system's full life cycle. By analyzing the reliability level of a stand-alone product cluster during a single mission during the full life cycle of the system, namely the average mission reliability of the stand-alone product, the reliability design index of the stand-alone product can be determined. Thus, through system synthesis, the reliability level of the reusable suborbital vehicle system during a single mission can be obtained.

[0121] Preferably, based on the calculation formulas for the average task reliability of reusable products with exponential distribution and Weibull distribution, average task reliability modeling of reusable products with exponential distribution and average task reliability modeling of reusable products with Weibull distribution can be carried out.

[0122] (31) Modeling of average mission reliability of exponentially distributed reusable products.

[0123] For individual products (parts), their reliability characteristics vary. The reliability characteristics of the product should be determined based on the analysis and identification of the product's main failure modes and failure mechanisms, and the product reliability assessment model should be clarified. Generally, a unit reliability assessment model should be established based on the mission profile, different environmental conditions, mission functions, and time requirements experienced during the actual mission, and according to the main failure modes of the product during the use phase (life cycle). Taking products with accidental failures as an example, their main failure mode is accidental failure, and the exponential distribution model is generally used.

[0124] Average mission reliability evaluation model R for exponentially distributed reusable products + (t + ) is represented as follows:

[0125]

[0126] Where λ represents the failure rate of exponentially distributed reused products, t + It represents the task time of exponentially distributed reusable products. Assume that the total cumulative test time of exponentially distributed reusable products is T +, the number of failures is r + It should be noted that when a product is reusable, we need to calculate the total cumulative testing time based on the product's actual state, including testing time and usage time. Similarly, the failure count should also include both test failures and actual failures that occurred during the usage phase.

[0127] According to the following formula (2), the point estimate of the failure rate of exponentially distributed reused products is calculated:

[0128]

[0129] According to the following formula (3), the upper limit of the failure rate of exponentially distributed reused products λ is calculated U :

[0130]

[0131] According to the following formula (4), the estimated reliability value of the exponential distribution type reused product is calculated:

[0132]

[0133] According to the following formula (5), the lower limit of the reliability of the exponential distribution type reused product R is calculated. L :

[0134]

[0135] Then, the normalized reliability function cumulative probability distribution function of exponentially distributed reused products is recorded as

[0136]

[0137] Similarly, the confidence lower limit of the normalized reliability cumulative probability distribution function of the exponential distribution type reusable product can be obtained. That is, the above formula can be used to observe that the product can complete the task at the specified observation point by making full use of the test data information and historical usage data information in different tasks of the reusable task, different stages of the same task, or different time nodes. represents the starting time of the exponentially distributed repeated use product task, Indicates the end time of the exponentially distributed repeated use product task.

[0138] (32) Modeling of average mission reliability of reusable products with Weibull distribution.

[0139] For example, products subject to wear-out failures primarily fail due to wear-out, and the Weibull distribution model is generally used. This model is suitable for products such as gyro motors, bearings, and engines. When conducting failure mode analysis for products with a Weibull distribution, it's important to understand that within a relatively short mission cycle, the product's performance may remain perfectly good. It's even possible that the product's performance during its second or third mission may be even better than during the previous missions. This is because wear-out products experience a period of initial adjustment, followed by a plateau period, before truly entering the wear-out phase. In practical applications, specific products (such as gyro motors, bearings, and engines) may experience random failures during the first few missions. Therefore, specific parameters of the Weibull distribution need to be assigned for each specific mission. A more engineering-oriented approach is to use an exponential distribution model for reliability modeling before significant wear-out, and a Weibull distribution for reliability modeling during the wear-out phase. A more conservative approach is to use the Weibull distribution throughout the reliability modeling process. For Weibull distribution products, we can use the Weibull Bayesian method for reliability modeling, which can make full use of information from multiple sources to achieve accurate evaluation of product reliability.

[0140] Weibull distribution average mission reliability evaluation model R for reusable products - (t - ) is represented as follows:

[0141]

[0142] Where η represents the characteristic life point of the Weibull distribution type repeated use product, m represents the shape parameter, t - It represents the failure time of Weibull distribution repeated use products; assuming that the number of Weibull distribution repeated use products in the test and actual use is n - , the number of failures is r - .

[0143] According to the following formula (8), the estimated failure time of the Weibull distribution repeated use product is calculated

[0144]

[0145] in, represents the pth Weibull distributed repeated use product failure time.

[0146] According to the following formula (9), the point estimate of the characteristic life of the Weibull distribution reused product is calculated:

[0147]

[0148] According to the following formula (10), the estimated reliability value of the Weibull distribution reused product is calculated as

[0149]

[0150] According to the following formula (11), the lower limit of the reliability of the Weibull distribution reused product is calculated

[0151]

[0152] Then, the normalized reliability function cumulative probability distribution function of the Weibull distribution type reused product is recorded as

[0153]

[0154] Similarly, the confidence lower limit of the normalized reliability cumulative probability distribution function of the Weibull distribution type reusable product can be obtained. That is, the above formula can be used to observe that the product can complete the task at the specified observation point by making full use of the test data information and historical usage data information in different tasks of the reusable task, different stages of the same task, or different time nodes. represents the starting time of the Weibull distribution repeated use product task, represents the end time of the Weibull distribution repeated use product task, express The point estimate of the characteristic life span at a given moment, express Point estimate of characteristic lifetime at a moment.

[0155] Step 4: Conduct single mission reliability modeling of the reusable suborbital vehicle system.

[0156] In view of the characteristics of a single mission of a reusable suborbital spacecraft system, a single mission reliability modeling work is carried out. Since the products at all levels of a single mission system should theoretically be in normal working condition, but when the system reaches the late stage of its life cycle, the reliability level of its products at all levels is in a degraded state, therefore, when considering the reliability modeling of a single mission of the system, it is necessary to consider the specific stage of the spacecraft system's full life cycle when a single mission is executed, that is, before carrying out the reliability modeling of a single mission of a reusable suborbital spacecraft system, it is necessary to clarify the state of the system. In other words, it is necessary to consider the number of missions that have been executed before the system executes a single mission. On this basis, the reliability levels of the products at all levels of the system and the reliability models of all levels of the system when executing a specific single mission are deconstructed and integrated. Specifically:

[0157] (41) Construction of reliability model and reliability data collection for single-machine products.

[0158] For stand-alone products, we will take exponentially distributed products as an example.

[0159] a) Collect the mission execution time and historical data of long-life exponential distribution products, including fault and failure data.

[0160] b) For long-life exponentially distributed products, after determining that the product is within its life cycle and meets the reliability index requirements of the task to be performed, the reliability level of the exponentially distributed product in completing the task is calculated using formula (6) in step 3.

[0161] (42) Deconstruction and synthesis of system reliability models.

[0162] a) Based on the functions that the system needs to perform in each subtask in a single task, clarify the key subsystems and key products involved in the implementation stage of each subtask, and at the same time clarify the redundant backup relationship of products at each level (deconstruction).

[0163] b) Based on the system function deconstruction analysis and according to the specific conditions of each subtask, select the appropriate modeling method to construct the subtask reliability model. It is usually recommended to use fault tree, dynamic fault tree, Bayesian method, etc.

[0164] c) According to the system's single mission profile, an event tree is used to model the mission process, and the reliability models of each subtask are integrated into the event tree model to form a comprehensive single mission reliability model.

[0165] Step 5: Conduct reliability modeling of the reusable suborbital vehicle system's full life cycle reusable mission.

[0166] The exponential distribution type reused product average mission reliability model and the Weibull distribution type reused product average mission reliability model obtained in step 3, combined with the benchmark model obtained in step 4, provide input for the dynamic modeling of the system's full life cycle reuse mission reliability.

[0167] Modeling the reliability of a reusable suborbital vehicle system throughout its lifecycle involves evaluating the system's ability to perform individual missions throughout its lifecycle, under assumed maintenance support conditions. This evaluation identifies weaknesses that affect the system's mission performance during each mission. This identification of weaknesses in mission performance provides a basis for optimizing system design, operation (maintenance support), and other aspects. Therefore, modeling the reliability of a reusable suborbital vehicle system throughout its lifecycle requires the following assumptions: a) Long-life products will not fail throughout the system's lifecycle, so the dynamic modeling of the lifecycle requires identifying which products are long-life products. b) Life-limited products are regularly replaced throughout the system's lifecycle, so the number and timing of repair and replacement of life-limited products must be specified in the dynamic modeling of the lifecycle, thereby clarifying the composition and status of each level of the system's products throughout their lifecycle.

[0168] Due to the complexity of the system, the reusable suborbital vehicle system as a whole exhibits random failure characteristics. Therefore, an implicit exponential distribution function can be used to characterize the system's mission reliability. However, constructing an exponential distribution function expression for a complex system is unrealistic. Therefore, it is necessary to construct a comprehensive mission reliability model for the complex system, which mainly includes three aspects:

[0169] (51) Process-based task modeling.

[0170] The process-based mission modeling is directly related to the implementation of the reusable suborbital vehicle system's full life cycle reusable mission. It is planned to conduct modeling assessments in a phased manner, such as Figure 3 As shown:

[0171] a) Modeling of typical single mission processes throughout the system life cycle.

[0172] First, the typical single mission profile of the system's full life cycle is analyzed and obtained. The typical single mission reliability modeling method of the reusable suborbital vehicle system described in step 4 is used to model the single mission process and obtain a system single mission reliability assessment model, namely, the system's full life cycle reusable mission reliability benchmark model.

[0173] b) Modeling of the system's full life cycle (multiple) mission processes.

[0174] The implementation of all missions throughout the system's life cycle is analyzed, clarifying the involvement of all system-level products in each mission, including product maintenance and replacement. Based on this, modeling of the system's full life cycle (multiple missions) is performed. Specifically, two scenarios are considered: i) During the early stages of system development or deployment, due to the diverse types of missions the system will perform, the specific mission profiles for each mission will also differ. Therefore, during these early stages, a "typical single mission profile" can be used to represent the different flight missions throughout the system's life cycle. These missions can be unified into a "typical single mission profile" based on the maximum mission profile envelope. This simplified approach reduces the complexity of modeling the system's full life cycle (multiple missions). ii) During the mid-to-late stages of system deployment, due to the changing types of missions performed by the system, multiple typical mission types can be summarized and organized. Based on preliminary mission planning, these multiple typical mission types can be distributed throughout the system's life cycle. In this case, dynamic modeling requires considering the differences in the single task process model caused by changes in task types (e.g., different subtasks involve different systems and subsystems, and different subtask execution tasks and functions, etc.).

[0175] (52) Function-based fault modeling.

[0176] For a reusable suborbital vehicle system, the system mainly includes basic functional subsystems and extended functional subsystems, and provides corresponding emergency return capabilities under emergency conditions. When modeling functions, it is necessary to consider the coupling relationship between subsystems and the coupling relationship between different functions. The input information for coupling relationship modeling mainly comes from the reusable suborbital vehicle system mission profile and reuse analysis results obtained in step 1 and the reusable suborbital vehicle system reliability typical characteristic score results obtained in step 2.

[0177] (53) Process-based maintenance modeling.

[0178] a) A reusable suborbital vehicle system includes a certain number of repairable and replaceable products. For pre-shipment reliability assessments (before first flight or finalization), the details of the repair process can be disregarded, and the modeling can be performed using a "repaired as new" approach. b) When considering the reusable suborbital vehicle system's ability to rapidly turn around between missions, the repair and replacement times for different products at each level need to be considered. Therefore, when rapid turnaround is not a consideration, the system's full lifecycle reliability modeling can be performed without modeling the specific repair process; only the status of the products in the model can be updated.

[0179] Step 6: Analyze and determine the average mission reliability of the reusable suborbital vehicle system over its entire life cycle.

[0180] For a reusable suborbital vehicle system, its lifespan T′ is theoretically a random variable, and the probability that the lifespan exceeds the specified time T″ is It is called the reliability function of the product R(T″), which is:

[0181]

[0182] For a single task, the normalized cumulative probability distribution function of the reliability function within the single task period (L) is recorded as R a ,have:

[0183]

[0184] Among them, 0<R a ≤1.

[0185] For repeated tasks, set the start time of the i-th task to t is , the task end time is t ie , then the cumulative probability distribution function of the reliability function in the i-th mission cycle is recorded as R ia ,have:

[0186]

[0187] The normalized cumulative probability distribution function of the reliability function is recorded as have:

[0188]

[0189] Furthermore, it can be clarified that the mission reliability evaluation model required to perform missions throughout the life cycle of the reusable suborbital spacecraft system is different in terms of the specific mission profiles, the differences in the product composition of each level of the system, the upgrade and iteration of related products, and the accumulation of related data. Specifically, it shows the characteristics of a piecewise function, which involves the differences in the system mission reliability evaluation model (implicit function), the differences in the values ​​of the variables related to the system reliability evaluation function, and the differences in the time variables caused by the start and end time of a single mission cycle. We can use the following function to illustrate the mission reliability evaluation function of the reusable suborbital spacecraft system throughout its life cycle. Since the system mission reliability reflects the ability of the system to complete the task when performing a specific task, the quantitative value of the system mission reliability probability is defined as R ys , we can get:

[0190]

[0191] in, represents the function related to the reliability of the kth subsystem, k = 1, 2, ..., K, K represents the number of subsystems in the system; α, β, γ are abstract parameters related to the reliability of the subsystem; Indicates the time t between the kth subsystem and the system's full life cycle s and other functions related to time-related parameter variables t′; t is and t ie It represents the start and end time of the system executing a single flight mission, and i represents the number of times the system repeats the mission.

[0192] Step 7: Analyze and determine the weak links in mission reliability of the reusable suborbital vehicle system throughout the entire mission process.

[0193] According to steps 4 to 6, a task reliability change trend diagram of the reusable suborbital vehicle system during the entire mission process is obtained through simulation analysis. Further, based on the task reliability change trend diagram of the reusable suborbital vehicle system during the entire mission process obtained through simulation analysis, it is determined that:

[0194] (71) The upper and lower bounds of the probability quantification “function” of task reliability during the entire task process.

[0195] Since the "function" (an implicit function) that quantifies the probability of task reliability throughout the entire mission is bounded, the upper and lower bounds of the bounded function can be determined based on the definition of the bounded function. The upper bound expresses the maximum value of the reliability probability quantization when executing a task within the system's full life cycle. This value is usually during the first task execution, generally at the starting point of the first task execution. The lower bound expresses the minimum value of the system's task reliability bounded function, that is, the minimum value of the task reliability. In addition, it is necessary to further determine the upper and lower bounds of the system's task reliability bounded function within a certain period of time within the system's full life cycle.

[0196] (72) System reliability weak intervals and interval extremes.

[0197] like Figure 4 As shown in the figure, within a specific time interval, the system mission reliability level may fall below a certain value (the mission reliability index requirement value). Based on the mission reliability trend chart throughout the entire mission process and the mission reliability index requirement value, the system reliability weak interval is determined. Within this weak interval, the system mission reliability level does not meet the specified mission reliability index requirement, and the system faces a significant risk in executing the mission. Within the system reliability weak interval, the interval extreme is identified, which is the minimum value of the system reliability probability quantization value within this weak interval (the interval extreme value).

[0198] (73) The work on the weak links of reliability during the system's mission execution is to clarify the weak links of reliability of the system throughout the entire mission process.

[0199] The reliability weak link analysis and determination during the system execution of the mission is mainly carried out according to the following methods:

[0200] a) Weak link identification method based on risk importance analysis

[0201] Risk importance calculation is used to evaluate the sensitivity of task risk value to changes in the probability of occurrence of basic events. Common risk importance calculation methods include Fussell-Vesely method, risk reduction equivalent method, risk increase equivalent method, etc. Fussell-Vesely method is used to calculate the risk of a task containing basic events (such as the bottom event of the fault tree) x i The importance of the minimum cut set in the total risk.

[0202]

[0203] in, Represents event x i FV importance, Indicates that event x is included i The probability of the union of the minimum cut sets of Represents the desired risk baseline.

[0204] When you need to identify a weak subtask in a single task or multiple tasks, you can calculate the risk importance of the intermediate event (stage subtask). The specific method is as follows:

[0205]

[0206] in, Indicates the FV importance of the ith intermediate event (stage subtask) in the event tree model, PLOM i represents the task failure probability of the i-th intermediate event (stage subtask), r M Represents the total probability of task failure.

[0207] b) Based on the actual situation, Fussell-Vesely importance calculation, risk reduced value (RRW), Birnbaum, risk achieved value (RAW) and differentiation methods are selected as the weak link identification methods.

[0208] Step 8: Analyze and determine the factors that affect the rapid turnaround of full-profile mission analysis.

[0209] Based on the reliability weaknesses of the system during the entire mission process and the characteristics of the impact on the system's rapid turnover capability during mission execution, the rapid turnover influencing factors corresponding to the reliability weaknesses are divided into: direct influencing factors, indirect influencing factors, and main influencing factors. Among them:

[0210] (81) Direct influencing factors refer to factors that directly affect the rapid turnover capability during the rapid turnover period of the system during the execution of tasks. Specifically, they include products that need to be repaired between single tasks due to insufficient reliability (products at all levels of the system identified by reliability weak links). Through product-oriented analysis, from various product dimensions such as system level, subsystem level, and single machine level, based on measured data and simulation analysis results, the reliability and life weak links in the task execution process are identified. It should be pointed out that the life here refers to the reliable life of the product.

[0211] (82) Indirect influencing factors refer to factors other than the level of product reliability, such as maintenance and support capabilities, and weak links in inspection and maintenance. Through a process-oriented, full-mission profile analysis approach, starting from the perspective of mission facts, based on the launch, flight, inspection and maintenance process dimensions, the impact on mission success or failure or inspection and maintenance is considered, and the key process links and related products for rapid system-level turnover are identified. It should be pointed out that indirect influencing factors mainly consider the quantifiable influencing factors corresponding to maintenance and support capabilities and inspection and maintenance capabilities.

[0212] (83) The main influencing factors refer to the comprehensive direct and indirect influencing factors. Through simulation analysis, under the constraints of system reliability index requirements and rapid turnover capability requirements, the correlation analysis of the identified weak links at the single-machine level, subsystem level, and system level is carried out. Combined with relevant parameters such as maintenance support capability and detection and maintenance capability, the weight of the weak links is analyzed and calculated based on the method of determining the principal components in multidimensional data. The principal component analysis method is used to take the contribution rate of the weak links at each level to the rapid turnover parameters as the weight coefficient. The comprehensive evaluation value is obtained by weighted summation of the principal components, and finally the most critical weak link with the comprehensive impact is obtained, that is, the main influencing factor.

[0213] Among them, direct influencing factors are characterized by reliability and life characteristic parameters, and indirect influencing factors are characterized by maintenance support capability and detection and maintenance capability parameters.

[0214] In summary, the method for identifying factors affecting rapid turnover based on full-profile mission analysis described in the present invention is different from the traditional system safety and reliability simulation. This method can reflect the characteristics of dynamic changes in the safety and reliability status of the system under the coupling effect of multiple factors. It clarifies the safety and reliability domains that may be involved in remote operation tasks in the form of domain analysis, and clarifies an analysis process for determining the impact domain of equipment, human errors and abnormal environmental conditions for mission implementation. After combining the relevant method process with the traditional method process, it can provide technical method support and decision-making analysis basis for the safety and reliability analysis and design of model-related remote operation tasks.

[0215] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solutions 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 solutions of the present invention shall fall within the scope of protection of the technical solutions of the present invention.

[0216] The contents not described in detail in the specification of the present invention belong to the common knowledge of professionals in this field.

Claims

1. A method for identifying factors affecting rapid turnover based on full-profile task analysis, characterized in that: include: Analyze and determine the mission profile and reuse conditions of reusable suborbital spacecraft systems; Analyze and determine the typical reliability characteristics of reusable suborbital spacecraft systems; Conduct average mission reliability modeling for single-unit products of reusable suborbital spacecraft systems; Conduct single mission reliability modeling of reusable suborbital spacecraft systems; Conducting reusable mission reliability modeling for the full life cycle of reusable suborbital spacecraft systems; Analyze and determine the average mission reliability of the reusable suborbital vehicle system throughout its life cycle; Analyze and identify weaknesses in mission reliability during the entire mission of the reusable suborbital vehicle system; Analyze and determine the factors that influence the rapid turnaround of full-profile mission analysis.

2. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 1 is characterized in that: Analyze and determine the mission profile and reuse conditions of the reusable suborbital vehicle system, including: According to the flight process of the reusable suborbital vehicle, the mission profile of the reusable suborbital vehicle system is divided into: take-off phase, ascent phase, orbit insertion phase, on-orbit flight phase, deorbit phase and unpowered return phase; Conduct single mission profile analysis; the single mission profile analysis considers the implementation of each subtask process of a typical single mission of the system and conducts the analysis work according to the process-based analysis method; Conduct repeated mission profile analysis, including: system composition analysis, clarify the factors affecting reliability during the system mission process: human factors, software and hardware equipment factors, and environmental factors; and provide multiple mission profile process diagrams.

3. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 2 is characterized in that: Analyze and determine the typical reliability characteristics of reusable suborbital spacecraft systems, including: Analysis of incomplete system reuse characteristics, including analysis of fully reusable products, partially reusable products, and single component reusability; combining the analysis results of fully reusable products, partially reusable products, and single component reusability to identify reusable subsystems, key subsystems, and related stand-alone products; Analysis of incomplete reuse characteristics of subsystems, including: Based on the identified reuse subsystems, key subsystems, and related stand-alone products, clarify the reuse characteristics of the subsystem throughout the entire mission cycle, including the number of reuses and the usage cycle time; Analysis of the lifespan and reliability characteristics of single-unit products; The maintainability analysis of individual products includes: allocating system maintainability indicators based on the rapid turnover cycle requirements of reusable suborbital vehicle systems, further clarifying the requirements related to the maintainability of individual products through the indicator allocation, and determining the results of the maintainability analysis of individual products; based on the results of the maintainability analysis of individual products, providing the maintenance timing for products at each level and determining the maintenance classification of individual products: condition-based maintenance, planned / preventive maintenance, and corrective maintenance.

4. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 3 is characterized in that: The average mission reliability modeling of a single product of a reusable suborbital vehicle system includes: the average mission reliability modeling of an exponential distribution type of reusable product and the average mission reliability modeling of a Weibull distribution type of reusable product.

5. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 4 is characterized in that: The modeling process of average mission reliability of exponentially distributed reusable products is as follows: Determine the average mission reliability evaluation model R of exponentially distributed reusable products + (t + ): Where λ represents the failure rate of exponentially distributed reused products, t + Represents the exponentially distributed repeated use of product task time; According to the following formula (2), the point estimate of the failure rate of exponentially distributed reused products is calculated: Among them, r + represents the exponentially distributed failure rate of repeated-use products, T + Indicates the total cumulative test time of exponentially distributed reusable products; According to the following formula (3), the upper limit of the failure rate of exponentially distributed reused products λ is calculated U : According to the following formula (4), the estimated reliability value of the exponential distribution type reused product is calculated: According to the following formula (5), the lower limit of the reliability of the exponential distribution type reused product R is calculated. L : Then, the normalized reliability function cumulative probability distribution function of exponentially distributed reused products is recorded as in, represents the starting time of the exponentially distributed repeated use product task, Indicates the end time of the exponentially distributed repeated use product task; The modeling process of the average mission reliability of Weibull distribution reused products is as follows: Determine the average mission reliability evaluation model R of Weibull distribution reused products - (t - ): Where η represents the characteristic life point of the Weibull distribution type repeated use product, m represents the shape parameter, t - represents the failure time of a Weibull-distributed repeated-use product; According to the following formula (8), the estimated failure time of the Weibull distribution repeated use product is calculated Among them, n - The number of products representing the Weibull-distributed repeated use of the product in the test and actual use, represents the pth Weibull distribution repeated use product failure time; According to the following formula (9), the point estimate of the characteristic life of the Weibull distribution reused product is calculated: Among them, r - represents the number of Weibull-distributed repeated-use product failures; According to the following formula (10), the estimated reliability value of the Weibull distribution reused product is calculated as According to the following formula (11), the lower limit of the reliability of the Weibull distribution reused product is calculated Then, the normalized reliability function cumulative probability distribution function of the Weibull distribution type reused product is recorded as in, represents the starting time of the Weibull distribution repeated use product task, represents the end time of the Weibull distribution repeated use product task, express The point estimate of the characteristic life span at a given moment, express Point estimate of characteristic lifetime at a moment.

6. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 5 is characterized in that: Conduct single mission reliability modeling of reusable suborbital vehicle systems, including: Build reliability models and collect reliability data for stand-alone products; The deconstruction and synthesis of the system reliability model include: for the functions that the system needs to perform in each subtask in a single task, clarify the key subsystems and key products involved in the implementation stage of each subtask, and clarify the redundant backup relationship of products at each level; according to the specific situation of each subtask, select the corresponding modeling method to construct the subtask reliability model; according to the profile of the system executing a single task, use the event tree to model the task process, and integrate the reliability models of each subtask into the event tree model to form a comprehensive model of single task reliability.

7. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 6 is characterized in that: Conducting full life cycle reusable mission reliability modeling for reusable suborbital spacecraft systems, including: The following assumptions are made: long-life products will not fail during the system's life cycle; limited-life products will be replaced regularly during the system's life cycle; Based on the given assumptions, process-based task modeling, function-based fault modeling, and process-based maintenance modeling are carried out.

8. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 7 is characterized in that: When analyzing and determining the average mission reliability of the reusable suborbital spacecraft system throughout its life cycle, the system mission reliability probability quantified value is defined as R ys : Among them, S subk represents the function related to the reliability of the kth subsystem, k = 1, 2, ..., K, K represents the number of subsystems in the system; α, β, γ are abstract parameters related to the reliability of the subsystem; Indicates the time t between the kth subsystem and the system's full life cycle s and other functions related to time-related parameter variables t′; t is and t ie It represents the start and end time of the system executing a single flight mission, and i represents the number of times the system repeats the mission.

9. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 8, characterized in that: Analyze and identify weaknesses in mission reliability throughout the entire mission of the reusable suborbital vehicle system, including: Based on the single mission reliability modeling of the reusable suborbital spacecraft system, the reusable mission reliability modeling of the reusable suborbital spacecraft system throughout its life cycle, and the average mission reliability of the reusable suborbital spacecraft system throughout its life cycle, a mission reliability change trend diagram of the reusable suborbital spacecraft system throughout its mission process is obtained through simulation analysis; Based on the mission reliability change trend diagram of the reusable suborbital vehicle system throughout the entire mission process obtained from simulation analysis, the upper and lower bounds of the mission reliability probability quantification "function" for the entire mission process, the system reliability weak interval and interval extremes, and the reliability weak links during the system's mission execution are determined to clarify the reliability weak links of the system throughout the entire mission process.

10. The method for identifying factors affecting rapid turnover based on full-profile task analysis according to claim 9, characterized in that: Analyze and determine the factors that influence the rapid turnaround of full-profile mission analysis, including: Based on the identified reliability weaknesses of the system throughout the entire mission process and the characteristics that influence the system's rapid turnover capability during mission execution, the rapid turnover influencing factors corresponding to the reliability weaknesses are divided into: Direct influencing factors, indirect influencing factors, and main influencing factors.

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