Reliability collaborative design and evaluation method and system for reusable rocket maintenance

Abstract

The application provides a reusable rocket maintenance reliability collaborative design and evaluation method and system, relates to the technical field of spaceflight equipment, and comprises the following steps: inputting system-level average repair time, product function structure tree and multiple device initial failure rates of multiple devices to be analyzed, performing weighted factor recursion calculation, and outputting an MTTR distribution table; performing maintenance process simulation, outputting a man-machine ergonomics analysis report, maintenance task estimated time and task complexity analysis results; calculating multiple risk indexes RAC values of multiple maintenance tasks; performing risk classification, and outputting multiple task risk levels; performing priority sorting, and outputting a maintenance task priority sequence list. The application solves the technical problem in the prior art that maintainability design is usually analyzed after main design solidification such as structure layout, the intervention time of maintainability analysis is delayed, reliability defects are difficult to completely solve in the later design, and thus the maintenance efficiency and the overall availability of the rocket are affected.

Description

Technical Field

[0001] This invention relates to the field of aerospace equipment technology, specifically to a reliability collaborative design and evaluation method and system for reusable rocket maintenance. Background Technology

[0002] In the development of reusable rockets, achieving rapid maintenance and turnaround is crucial for achieving high availability and low cost. However, current aerospace design and analysis methods for maintenance reliability have significant limitations: First, traditional methods are mostly "late-stage verification-based," with maintainability analysis often only introduced after major designs such as structural layout have been finalized. This makes it difficult to fundamentally optimize discovered reliability defects (such as insufficient operating space or obstructed maintenance access), severely restricting maintenance efficiency. Second, existing methods heavily rely on designers' qualitative experience and lack a scientific process to transform the vague requirement of "easy maintenance" into precise quantitative indicators and unified design criteria, resulting in highly subjective and difficult-to-verify design outcomes. Furthermore, the professional fields of structural design, maintainability engineering, and ergonomics operate in isolation, lacking a collaborative iterative mechanism aimed at achieving "optimal reliability," leading to frequent design conflicts. Summary of the Invention

[0003] This application provides a reliability co-design and evaluation method and system for reusable rocket maintenance, aiming to solve the technical problem that the maintainability design of existing technologies is usually analyzed only after the main design such as structural layout has been solidified, which delays the intervention of maintainability analysis and makes it difficult to completely solve reliability defects in the later design, thereby affecting maintenance efficiency and the overall availability of the rocket.

[0004] The first aspect disclosed in this application provides a reliability co-design and evaluation method for reusable rocket maintenance. The method includes: inputting system-level mean time to repair (MTTR), product functional structure tree, and initial failure rates of multiple devices to be analyzed; recursively calculating and outputting an MTTR allocation table using weighted factors; inputting a digital prototype, a maintenance task list, and the MTTR allocation table into a human-machine simulation environment; performing maintenance process simulation; outputting a human-machine ergonomics analysis report, estimated maintenance task time, and task complexity analysis results; using the estimated maintenance task time and task complexity analysis results as input, calculating multiple risk indices (RAC) values ​​for multiple maintenance tasks using a risk assessment matrix; classifying the risk of the multiple RAC values ​​according to a preset RAC threshold; outputting multiple task risk levels; and prioritizing the multiple maintenance tasks according to the multiple RAC values ​​and multiple task risk levels, outputting a maintenance task priority sequence table.

[0005] The second aspect disclosed in this application provides a reliability collaborative design and evaluation system for reusable rocket maintenance. This system is used in the aforementioned reliability collaborative design and evaluation method for reusable rocket maintenance. The system includes: a quantitative index decomposition module, used to input system-level mean time to repair (MTTR), product function structure tree, and initial failure rates of multiple devices to be analyzed, and output an MTTR allocation table through weighted factor recursion calculation; a qualitative design and simulation module, used to input a digital prototype, maintenance task list, and the MTTR allocation table into a human-machine simulation environment, perform maintenance process simulation, and output a human-machine ergonomics analysis report, estimated maintenance task time, and task complexity analysis results; and a risk assessment and decision-making module, used to take the estimated maintenance task time and task complexity analysis results as input, calculate multiple risk indices (RAC) values ​​for multiple maintenance tasks through a risk assessment matrix, classify the risk of the multiple RAC values ​​according to a preset RAC threshold, output multiple task risk levels, and prioritize the multiple maintenance tasks according to the multiple RAC values ​​and multiple task risk levels, outputting a maintenance task priority sequence table.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By inputting the system-level mean time to repair (MTTR), product function structure tree, and initial equipment failure rate, a weighted factor recursive calculation table is derived in the DELMIA system, laying a data foundation for subsequent design and analysis. By decomposing MTTR indicators at each level, maintenance time and task complexity are quantified, improving the objectivity and consistency of the design. By inputting the digital prototype, maintenance task list, and MTTR allocation table into the human-machine simulation environment, maintenance process simulation is performed, outputting a human-machine ergonomics analysis report and estimated maintenance task time. This provides clear quantitative targets for the design, avoiding the empirical ambiguity of traditional methods. The risk index (RAC) values ​​of multiple maintenance tasks are calculated using a risk assessment matrix. Based on preset RAC thresholds, the risk indices of multiple maintenance tasks are graded, and task priorities are ranked. This allows for the scientific determination of which tasks should be prioritized when facing a large number of maintenance tasks. This optimization not only helps to rationally allocate maintenance resources but also effectively controls high-risk tasks that may occur during maintenance, reducing uncertainty and potential risks in the overall maintenance process. Through precise task allocation and optimization, inspection, maintenance, and repair time can be effectively shortened, thereby significantly reducing operating costs and improving the reliability and operational efficiency of rocket maintenance.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the reliability co-design and evaluation method for reusable rocket maintenance provided in the embodiments of this application.

[0010] Figure 2 A schematic diagram of the reliability co-design and evaluation system for reusable rocket maintenance provided in this application embodiment.

[0011] Figure labeling: Quantitative index decomposition module 10, qualitative design and simulation module 20, risk assessment and decision-making module 30. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] Example 1, as Figure 1 As shown in the embodiments of this application, a reliability co-design and evaluation method for reusable rocket maintenance is provided, the method comprising:

[0014] A100: Input the system-level mean time to repair (MTTR), the product function structure tree, and the initial failure rates of multiple devices to be analyzed. Calculate the MTTR allocation table through weighted factor recursion.

[0015] The system-level mean time to repair (MTBT) is a top-level requirement set by the project management team based on mission requirements. For example, the repair time for the first stage of a rocket should not exceed 6 hours. The product function structure tree is a hierarchical model of the system, describing the hierarchical relationship between the system, subsystems, and equipment. For example, the rocket's propulsion system is divided into subsystems such as liquid oxygen valves, fuel pumps, sensors, and pipelines. Each piece of equipment to be analyzed has an initial failure rate, which is the probability that the equipment will fail within a unit of time.

[0016] Using the weighted allocation module, a weighting factor is calculated for each piece of equipment based on its failure rate, equipment attributes, maintenance requirements, and designed spatial layout. This weighting factor is used to adjust the equipment's maintenance priority and time budget. For example, the failure rate of the liquid oxygen main valve V-001 is V = 1.2 × 10⁻⁶. ―5 / h, the engineer assigns values ​​based on the attributes of V-001: the type belongs to "electromechanical equipment", so a value of 1.3 is assigned; based on the initial layout, a heat insulation cover needs to be removed to access it, so a value of 1.4 is assigned; the weighting factor is calculated as 1.2 × 10. ―5 ×1.3×1.4=2.184×10 ―5 Next, the same calculation is performed on all propulsion module devices, and the results are summed to obtain the total weight ΣW. ― j. Finally, the automatic calculation of the V-valve allocation index MTTR V ―001=6 hours × (2.184 × 10 ―5 / ΣW ― j) = 48 minutes. Repeat this process to generate an MTTR allocation table, which specifies the time budget for each device.

[0017] A200: Input the digital prototype, maintenance task list and the MTTR allocation table into the human-machine simulation environment, perform maintenance process simulation, and output human-machine ergonomics analysis report, estimated maintenance task time and task complexity analysis results.

[0018] A digital prototype is a virtual model used as a simulation platform to represent a system to be repaired, such as a rocket's propulsion module. The digital prototype includes information such as the system's physical characteristics, dimensions, layout, and equipment locations, providing a specific scenario for simulation. The maintenance task list lists all maintenance operations that need to be performed. The MTTR allocation table provides the time budget for each device or task as simulation input.

[0019] In the simulation environment, digital human body models are used to simulate the movements of maintenance personnel. These models have different percentile versions, representing maintenance personnel of varying body types and sizes. They are used to simulate maintenance personnel operating in confined spaces, ensuring the comfort and feasibility of the maintenance process can be assessed under various human conditions. During the maintenance process simulation, multi-percentile digital human body models are established and standard posture libraries are called upon. Simultaneously, posture feasibility analysis, dynamic interference checks, visual channel analysis, and maintenance channel analysis are performed.

[0020] Based on the results of the above four analyses, the output ergonomics analysis report lists the feasibility assessment of each task, such as whether there are comfort issues, interference blind spots, etc.; the estimated maintenance time is based on the standard time value of each basic action in the simulation, and the total maintenance time of each task is accumulated; the task complexity analysis results are based on factors such as interference, posture issues, and operation difficulty in the simulation, and qualitative and quantitative assessment of the complexity of the task.

[0021] For example, for the task of "replacing the liquid oxygen main valve V-001", the task sequence is defined as follows: ① Open hatch H-01 → ② Remove heat shield I-05 → ③ Disconnect the two electrical connectors → ④ Loosen the four quick-release clamps → ⑤ Remove the old valve → ⑥ Install the new valve → ⑦ Reverse steps ①-④. Next, a simulation scenario is established: In DELMIA, import the 95th percentile (tall) male human body model of the rocket digital prototype, set the maintenance tools as "torque wrench" and "clamp pliers", and assign them three-dimensional models. Next, a maintenance process simulation was performed: Posture verification: The digital human model was driven in a kneeling position to remove the heat shield I-05. The system showed that the waist bending angle exceeded the comfortable range and indicated a risk of interference with the bulkhead. Operating space analysis: When simulating loosening the clamps, the system dynamically generated the wrench's swing envelope space and found dynamic interference with adjacent pipelines. The simulation showed that when disconnecting the second electrical connector, the connector's locking indicator was in a blind spot. Maintenance access analysis: The system measured the minimum diameter of the "arm passage" through the hatch to the valve installation point to be 180mm, while the actual hatch diameter is 160mm, indicating insufficient passage width. Finally, time estimation and verification were performed: Based on the simulation's standard motion library, the system estimated the task would take 72 minutes, significantly exceeding the initial target of 48 minutes by 24 minutes. A simulation report was automatically generated, highlighting all the aforementioned issues.

[0022] A300: Taking the estimated time of the maintenance task and the result of the task complexity analysis as input, calculate multiple risk index (RAC) values ​​for multiple maintenance tasks through the risk assessment matrix, classify the risk of the multiple risk index RAC values ​​according to the preset RAC threshold, output multiple task risk levels, prioritize the multiple maintenance tasks according to the multiple risk index RAC values ​​and the multiple task risk levels, and output a maintenance task priority sequence list.

[0023] The risk assessment matrix uses rows to represent maintenance time levels (Level I to IV) and columns to represent failure probability levels (Level A to C). The matrix intersections correspond to the Risk Assessment Index (RAC). Each task is placed in a different position within the matrix based on its complexity and expected time consumption. For example, tasks with high complexity and long time consumption are located in the high-risk area, while tasks with low complexity and short time consumption are located in the low-risk area. The RAC value is determined by the task's position in the risk matrix; a higher RAC value indicates a greater risk, as tasks with higher complexity and time consumption have greater uncertainty and potential risks. The RAC value for each maintenance task is calculated based on the risk assessment matrix.

[0024] The preset RAC threshold is a predefined standard in the system used to classify tasks into different risk levels. Based on the preset RAC threshold, each risk index RAC value is assigned to the corresponding risk level, and the task risk level of each task is output. Each maintenance task corresponds to one risk level.

[0025] Maintenance tasks with high Risk Index (RAC) values ​​and high task risk levels require priority processing because they may have a significant impact on the overall safety and performance of the system. By sorting the RAC values ​​of each maintenance task and arranging them in descending order, all high-risk tasks are processed first, followed by medium-risk tasks, and finally low-risk tasks. The resulting maintenance task priority list includes the task name, RAC value, risk level, and sorted priority order. This list is provided to the maintenance team to adjust maintenance plans, ensuring that the most urgent and high-risk tasks begin earliest and minimizing the potential risk of system failure.

[0026] For example, first, input the simulation results: input the estimated time (72 minutes) and complexity (many steps, interference, poor visibility) of the "replace valve V-001" task into the RAC evaluation module. Second, assess the risk level: time level: 72 minutes > 60 minutes, rated as Level IV (extremely long); failure probability level: due to interference, blind spots, and uncomfortable posture, operation is prone to errors, rated as Level C (high). Next, calculate the RAC value: in the risk matrix, RAC = 12 corresponds to [IV, C]. Finally, make a risk decision: according to the preset RAC threshold, RAC ≥ 9 is unacceptable, the task is marked as "unacceptable risk" and listed first in the maintenance task priority list, recommending "design optimization is necessary".

[0027] Furthermore, the method involves inputting the system-level mean time to repair (MTTR), the product function structure tree, and the initial failure rates of multiple devices to be analyzed. Through recursive calculation using weighted factors, an MTTR allocation table is output. The method includes:

[0028] A110: Select the target subsystem based on the product function structure tree, and retrieve the target equipment list from the target subsystem.

[0029] A120: Summing up the initial failure rates of multiple devices to be analyzed in the target device list yields the subsystem failure rate.

[0030] A130: Calculate the subsystem MTTR budget value of the target subsystem based on the overall system failure rate, subsystem failure rate, and the system-level mean time to repair.

[0031] A140: Through the weighted allocation module, multiple device weighting factors are obtained by recursively calculating the weighting factors based on the multiple failure rates of the multiple devices to be analyzed and the multiple predefined device attribute factors.

[0032] A150: The total weight of the subsystem is obtained by summing the weighting factors of the multiple devices.

[0033] A160: Calculate and output the MTTR budget values ​​of multiple devices based on the total weight of the subsystem, the MTTR budget value of the subsystem, and the weighting factors of multiple devices.

[0034] A170: Associate the multiple devices to be analyzed and the multiple device MTTR budget values, and output the structured MTTR allocation table.

[0035] The product function structure tree displays the various levels and subsystems of product functions in a tree-like structure. Each node represents a functional module or subsystem, and child nodes represent smaller modules or components. Using the product function structure tree, a target subsystem is selected for analysis, such as a specific electromechanical system, electronic control unit, or hydraulic system. The selection of the target subsystem is based on the priority of fault analysis needs or maintenance plans. After selection, a list of all relevant equipment under the target subsystem is retrieved, including all subcomponents, modules, and units, which may involve different types of mechanical, electronic, and software systems.

[0036] Each device to be analyzed has an initial failure rate, expressed as a failure rate, which is the probability of the device failing per unit of time. The failure rate is expressed as the number of failures per hour. The initial failure rates of each device to be analyzed within the target subsystem are summed to obtain the overall subsystem failure rate, which serves as the basis for subsequent calculations.

[0037] The overall system failure rate refers to the total probability of a failure occurring in the entire system per unit time; it is the sum of the failure rates of all subsystems. The subsystem failure rate represents the frequency of failure in the target subsystem. The system-level mean time to repair (MTTR) is the average repair time for all maintenance tasks. The subsystem MTTR budget value for the target subsystem is obtained by dividing the subsystem failure rate by the overall system failure rate, and then multiplying that ratio by the system-level MTTR. This calculation implies that the target subsystem's repair time budget is related to the overall system failure rate and maintenance time; a higher subsystem failure rate requires more maintenance time. The subsystem MTTR budget value represents the expected maintenance time, used for subsequent maintenance planning, resource scheduling, and prioritization of maintenance tasks.

[0038] A weighting factor refers to the importance of a device to be analyzed relative to other devices in the maintenance plan. This weighting factor can be set based on multiple factors, including the device's failure rate, maintenance difficulty, the device's criticality to the entire system, the device's failure history, and the resources required for maintenance. The DELMIA system uses a recursive algorithm to calculate the weighting factor for each device to be analyzed step by step. For example, the higher the device's failure rate, the greater the maintenance difficulty, and the higher its criticality, the larger the weighting factor will be.

[0039] Each equipment weighting factor represents the importance of the equipment in the maintenance plan. Multiple equipment weighting factors are directly summed to obtain the total weight of the subsystem. This weight represents the weighted overall importance of the equipment in the entire subsystem.

[0040] The equipment MTTR budget value is obtained by dividing the equipment weighting factor by the total weight of the subsystem, and then multiplying the ratio by the subsystem MTTR budget value. This calculation method ensures that equipment with a larger weight will be allocated more maintenance time, that is, equipment with higher importance or greater maintenance time requirements will have more maintenance time.

[0041] Multiple devices to be analyzed are associated with their corresponding MTTR budget values. Devices can be matched with calculated MTTR budget values ​​by device ID or device name. A structured MTTR allocation table is output, which summarizes and displays information such as the name, weighting factor, and MTTR budget value of each device.

[0042] Furthermore, the method involves inputting the digital prototype, maintenance task list, and MTTR allocation table into a human-machine simulation environment, performing maintenance process simulation, and outputting a human-machine ergonomics analysis report, estimated maintenance task time, and task complexity analysis results.

[0043] A210: Decompose the maintenance task list to obtain an atomic-level sequence of operation steps.

[0044] A220: After loading the multi-percentage digital human body model and standard maintenance posture library into the human-machine simulation environment, import the digital prototype to activate the scene.

[0045] A230: In the human-machine simulation environment, based on the standard maintenance posture library as a constraint, when the multi-percentage digital human body model is driven to perform maintenance process simulation according to the atomic-level operation step sequence, four-dimensional analysis is performed simultaneously, and the human-machine ergonomics analysis report, maintenance task estimated time, and task complexity analysis results are output.

[0046] The maintenance task list includes multiple major task steps, encompassing equipment inspection, fault diagnosis, repair, and component replacement. Each major task can be further broken down into multiple atomic-level operation steps. These atomic-level operation steps are specific, minimal operations, such as "disassembling component A," "checking the voltage of line B," and "installing a new seal." All atomic-level operation steps are arranged according to the logical sequence of the maintenance process, forming an atomic-level operation step sequence. This sequence is a detailed execution plan for the maintenance task, determining the specific actions and order in which maintenance personnel perform each step.

[0047] Multi-percentile digital human models are a variety of standardized human models generated from datasets of different body types and sizes. In order to simulate the movements and efficiency of different workers when performing tasks, multiple digital human models need to be loaded to cover scenarios with different body types, heights and postures. These models can simulate the actual working conditions of people. For example, when short, tall or large people perform the same task, the required range of motion, posture and time will be different.

[0048] The standard maintenance posture library contains a series of recommended maintenance postures or movements that are defined based on ergonomics and practical maintenance experience. These posture libraries can help define the best postures that maintenance personnel should take when performing various operation steps.

[0049] A digital prototype is a 3D model that simulates the equipment to be repaired. It can realistically reproduce the equipment's appearance, structure, and operating environment. In the simulation environment, the digital prototype needs to work in conjunction with a digital human model and a pose library. Activating the scene means combining these elements in the simulation software to create a complete maintenance operation scenario, ready for simulation.

[0050] Based on an atomic-level sequence of operation steps, a multi-percentage digital human body model is driven to execute each maintenance operation. These operations are performed according to the optimal postures provided by a standard maintenance posture library, ensuring that the movements of each maintenance task are as ergonomic as possible, reducing personnel fatigue and potential work-related injuries. Four-dimensional analysis refers to the synchronous analysis of human movement in three-dimensional space over time. Four-dimensional analysis covers posture feasibility analysis, dynamic interference inspection, visual channel analysis, and maintenance channel analysis. Through four-dimensional analysis, potential risks and bottlenecks can be accurately identified, such as an operation step requiring unreasonable movements, or a maintenance position requiring prolonged high-intensity physical labor, all of which can affect personnel work efficiency and health.

[0051] The ergonomics analysis report includes ergonomic analysis during task execution, such as identifying unreasonable actions or excessively long operation times. The report helps identify potential inefficiencies and areas for improvement in the workflow. The estimated time for maintenance tasks is based on simulation processes and time analysis, providing time estimates for each maintenance task to help engineers and maintenance teams schedule tasks reasonably. The task complexity analysis results are based on the complexity of operation steps, the time required, and potential risks, outputting the complexity analysis results for each maintenance task. This helps prioritize tasks, identify which tasks are more complex or high-risk, requiring priority processing or additional resources.

[0052] Furthermore, the method also includes:

[0053] A240: Simultaneously perform four-dimensional analysis during the maintenance process and output four-dimensional analysis data, wherein the four-dimensional analysis covers attitude feasibility analysis, dynamic interference inspection, visual channel analysis and maintenance channel analysis.

[0054] A250: Based on the four-dimensional analysis data and the sequence of atomic-level operation steps, the task complexity is evaluated, and the task complexity analysis results are output.

[0055] A260: Extract posture comfort data, interference position data, and blind spot data from the four-dimensional analysis data, perform comprehensive human-machine ergonomic analysis, and output the human-machine ergonomic analysis report.

[0056] A270: By accumulating the maintenance task time during the maintenance process simulation, the estimated time of the maintenance task is output.

[0057] A280: Based on the task equipment association relationship, the estimated time of the maintenance task is mapped and compared with the MTTR budget value of the corresponding equipment in the MTTR allocation table, and a time difference report is output.

[0058] During the simulation, various data from the four-dimensional analysis are collected and processed in real time to form four-dimensional analysis data. Specifically, the analysis includes: Posture feasibility analysis: This analyzes whether the posture of maintenance personnel during each operation step conforms to ergonomic requirements, whether they can complete the operation comfortably and effectively, and checks for unreasonable body bending, overextension, or other movements to reduce potential musculoskeletal injuries; Dynamic interference check: This checks whether the maintenance personnel's movements interfere with equipment, tools, or other objects in the work environment. For example, it checks whether the maintenance personnel can smoothly reach the operating position, whether there are obstacles blocking the movement, or whether the tools can be used smoothly; Visual channel analysis: This assesses whether the maintenance personnel's field of vision is limited when performing the task, checks for blind spots, especially in complex or compact equipment areas, and ensures that maintenance personnel can see and operate all necessary components; Maintenance access analysis: This checks whether maintenance personnel can smoothly enter the work area for maintenance. This analysis considers the spatial layout, the structural design of the workbench or equipment, and whether personnel can easily enter and operate the target equipment when performing the task.

[0059] Task complexity assessment is based on factors such as the number of operation steps, the precision of the operation, the time requirements of the task, and the results of four-dimensional analysis. Based on four-dimensional analysis data, such as posture difficulty, interference issues, and visual issues, and the difficulty of the operation steps, a task complexity score is calculated. The task complexity is then categorized into multiple levels, such as simple, medium, complex, and high complexity. The task complexity analysis results help identify the most complex tasks requiring priority in subsequent task scheduling.

[0060] Posture comfort data: Extract human posture data for each operation step and analyze whether the operation meets ergonomic requirements. For example, excessive bending or stretching may cause discomfort and increase physical burden. Interference location data: Identify the interference locations that occur during maintenance and analyze which parts may restrict or hinder the movement of maintenance personnel. Blind spot data: Analyze the visual range of maintenance personnel and identify which parts of the field of vision are obstructed, which may lead to operational errors or inefficiency.

[0061] Based on the above data, the human burden, inefficiency, or operational difficulty that may occur during the execution of each maintenance task is assessed. Human-machine ergonomics analysis is used to evaluate whether each operation step needs to be adjusted to improve maintenance efficiency and reduce physical stress on maintenance personnel. Based on the analysis results, a human-machine ergonomics analysis report is output.

[0062] During the simulation, a time value is assigned to each atomic-level operation step. The calculation of the time value is based on the difficulty of each operation, the complexity of the required actions, and the four-dimensional analysis data. By accumulating the time of all atomic-level operation steps, the estimated time of the entire maintenance task is obtained.

[0063] Maintenance tasks are typically associated with specific equipment and subsystems. The estimated time for each task needs to consider the equipment's maintenance complexity and MTTR (Mean Time To Live) value. Based on the equipment attributes of the task, data from the corresponding MTTR allocation table is referenced. The estimated time of the maintenance task is compared with the value in the MTTR allocation table, and a time difference report is output based on the comparison results. This report indicates the discrepancy between the estimated time of the maintenance task and the time in the MTTR allocation table. The report includes difference analysis, possible resource issues, and potential causes of time delays.

[0064] Furthermore, using the estimated time and complexity analysis results of the maintenance tasks as input, the method calculates multiple risk indices (RAC) values ​​for multiple maintenance tasks through a risk assessment matrix. The method also includes:

[0065] A310: Based on the estimated time of the maintenance tasks, the maintenance time levels are divided to obtain multiple task time levels for the multiple maintenance tasks.

[0066] A320: Based on the results of the task complexity analysis, extract the number of multiple steps, multiple fine operation points and multiple environmental interference items of the multiple maintenance tasks, determine the failure probability level, and output the failure probability level of multiple tasks.

[0067] A330: Based on the multiple task time levels and multiple task failure probability levels, calculate the RAC value in the risk assessment matrix and output the multiple risk index RAC values.

[0068] Maintenance tasks are categorized into different time levels, for example: Level I (short-time tasks): estimated completion time is less than a certain range, such as within a few hours; Level II (medium-time tasks): estimated time is between a few hours and a day; Level III (long-time tasks): estimated time is between a day and several days; Level IV (extremely long-time tasks): estimated time exceeds several days and may require multiple maintenance operations. Based on the estimated time of the maintenance task, each task is assigned a time level (Level I to Level IV), which serves as an input parameter for the time dimension in subsequent risk assessments.

[0069] Number of steps: The more steps a task has, the higher its complexity usually is, and the greater the possibility of task failure. Fine-grained operation points represent the parts of the task that require high precision, and these operation points increase the risk of task failure. Environmental interference factors such as spatial limitations and tool usage restrictions can affect the successful completion of the task and increase the possibility of failure.

[0070] Based on these complexity factors, the likelihood of task failure is assessed and categorized into several levels. For example, Level A: Low likelihood of task failure, simple operation steps, low precision requirements, and minimal environmental interference; Level B: Moderate likelihood of task failure, numerous operation steps, certain fine-grained operation points, and moderate environmental interference; Level C: High likelihood of task failure, complex operation steps, involving fine-grained operation, and significant environmental interference.

[0071] The row dimension of the risk assessment matrix represents the maintenance time level (Level I to Level IV), and the column dimension represents the failure probability level (Level A to Level C). The intersections in the matrix represent a combination of a specific time level and a failure probability level, and each intersection corresponds to a risk assessment index (RAC value).

[0072] By mapping the time level and failure probability level of each maintenance task to the intersection points in a matrix, a Risk Index (RAC) value is calculated according to a preset risk scoring rule. A high time level and high failure probability level (e.g., Level IV time, Level C failure probability) correspond to a high RAC value, meaning the task has a higher risk; conversely, a low time level and low failure probability level (e.g., Level I time, Level A failure probability) correspond to a low RAC value, meaning the task has a lower risk. Based on the time level and failure probability level of each maintenance task, a Risk Index (RAC) value is calculated for each task, representing the overall risk of the task; a higher value indicates a greater risk.

[0073] Furthermore, if the RAC value of the Kth risk index for the Kth maintenance task does not meet the upper limit of the RAC threshold, closed-loop optimization is triggered until the updated RAC value of the Kth risk meets the upper limit of the RAC threshold.

[0074] The Kth maintenance task is any one of multiple maintenance tasks, used as the current analysis object. K is a positive integer. If the RAC value of the Kth maintenance task does not meet the set upper limit of the RAC threshold, it indicates that the risk of the task is too high, which may lead to delays, failures, or excessive resource consumption. In this case, the system automatically enters closed-loop optimization mode. At this time, a series of adjustment and optimization measures are taken to reduce the RAC value of the Kth risk index until it meets the RAC threshold. Closed-loop optimization is an iterative process that continuously optimizes the system design, task steps, maintenance plan, etc., and adjusts parameters until the risk value of the task is finally reduced to an acceptable range.

[0075] For example, first, an optimization cycle is triggered: based on the RAC assessment results, the design team initiates the optimization process. Next, design changes are implemented (feedback to the qualitative plan): For insufficient access: the diameter of hatch H-01 is increased from 160mm to 200mm. For operational interference: the V-valve mounting position is rotated 15 degrees, and adjacent piping is rerouted to provide sufficient operating space for the wrench. For blind spots: the electrical connector is replaced with a model featuring a lateral locking indicator. For uncomfortable posture: a retractable footrest is added below the maintenance area, allowing maintenance personnel to operate in a more comfortable semi-kneeling position. Next, simulation and evaluation are performed again: the optimized digital prototype is re-input into the simulation module of the second step. New simulation results show: all interferences are eliminated, visibility is good, posture is comfortable, and the estimated maintenance time is reduced from 72 minutes to 41 minutes. Next, a risk assessment is conducted again (feedback to the third step): Time level: 41 minutes, rated as Level III (long). Failure probability level: due to design optimization, complexity is significantly reduced, rated as Level B (medium). The new RAC value is obtained: [Ⅲ,B] corresponds to RAC=6. Finally, the iteration terminates: RAC=6 is considered "moderate risk," and although optimization is recommended, it is no longer "unacceptable." After review, the project team deemed this risk level acceptable, and the collaborative design and evaluation process for the "Replace Valve V-001" task concluded. All process data, models, and reports were automatically archived.

[0076] Furthermore, closed-loop optimization covers the iterative processes of design optimization loop, maintenance process simulation, and risk assessment.

[0077] The design optimization loop refers to reducing task complexity or improving the operability of the maintenance process by adjusting the physical design parameters of a digital prototype, such as maintenance hatches and equipment layout. After design optimization, maintenance process simulation is performed. By simulating the digital prototype, the impact of the new design and parameters on task execution is evaluated. For example, it verifies how the new design reduces interference, optimizes posture, or improves the maintenance environment. After simulation, the task risk under the new design is reassessed, and the Reliability and Acceptance Rate (RAC) value for each task is calculated based on the new data. If the RAC value still does not meet the requirements, the process returns to its previous state and the design is adjusted again until the threshold is met. The entire closed-loop optimization process is an iterative process. Each optimized design and simulation result will influence the starting point of the next evaluation, ensuring that each design adjustment minimizes the risk of the maintenance task.

[0078] Furthermore, the parameter tuning variables of the design optimization loop are the physical structural parameters of the digital prototype, including but not limited to the maintenance hatch diameter, equipment installation angle, pipeline layout topology, connector type, connector marking direction, support structure position, and support structure shape.

[0079] During the design optimization process, a series of physical structural parameters are adjusted to improve the feasibility and efficiency of task execution. These parameters include: hatch diameter, as the hatch size directly affects the operating space for maintenance personnel; appropriately increasing the hatch diameter can reduce space congestion and improve maintenance efficiency; equipment installation angle, as optimizing the equipment installation angle can improve the operating posture of maintenance personnel and reduce physical burden during operation; pipeline layout topology, as optimizing the pipeline layout can avoid pipeline interference, reduce obstacles during maintenance, and improve the smoothness of maintenance; connector type and marking direction, as improving the type and marking direction of connectors facilitates quick identification and connection by maintenance personnel, avoiding errors and misoperations; and support structure position and shape, as adjusting the design of the support structure can provide better support and stability, ensuring safety and reliability during maintenance.

[0080] After adjusting these parameters, simulations are used to verify whether the new design can effectively reduce task complexity, reduce interference, and improve maintenance efficiency. At the same time, the optimized design will bring new task risk assessments and recalculate the RAC value. Each adjustment and optimization will affect the task's RAC value. The ultimate goal is to reduce the task's risk level through these design optimizations and ensure that the RAC value of all tasks meets the preset safety threshold.

[0081] Example 2, based on the same inventive concept as the reliability co-design and evaluation method for reusable rocket maintenance in the foregoing examples, such as... Figure 2 As shown in the embodiments of this application, a reliability co-design and evaluation system for reusable rocket maintenance is provided, the system comprising:

[0082] The quantitative index decomposition module 10 is used to input the system-level average repair time, product function structure tree, and initial failure rates of multiple devices to be analyzed, and outputs an MTTR allocation table through weighted factor recursion calculation. The qualitative design and simulation module 20 is used to input the digital prototype, maintenance task list, and the MTTR allocation table into the human-machine simulation environment, execute maintenance process simulation, and output a human-machine ergonomics analysis report, estimated maintenance task time, and task complexity analysis results. The risk assessment and decision-making module 30 is used to calculate multiple risk indices (RAC) values ​​of multiple maintenance tasks through a risk assessment matrix, using the estimated maintenance task time and task complexity analysis results as input; classify the risk of the multiple risk indices (RAC) values ​​according to a preset RAC threshold, and output multiple task risk levels; prioritize the multiple maintenance tasks according to the multiple risk indices (RAC) values ​​and multiple task risk levels, and output a maintenance task priority sequence table.

[0083] Furthermore, the quantitative index decomposition module 10 is used to perform the following operation steps:

[0084] Based on the product function structure tree, a target subsystem is selected, and a target equipment list is retrieved from the target subsystem. The initial failure rates of multiple devices to be analyzed in the target equipment list are summed to obtain the subsystem failure rate. The subsystem MTTR budget value of the target subsystem is calculated based on the overall system failure rate, the subsystem failure rate, and the system-level mean time to repair (MTTR). Through a weighted allocation module, weighted factors are recursively calculated based on the multiple failure rates of the multiple devices to be analyzed and predefined multiple device attribute factors to obtain multiple device weighting factors. The multiple device weighting factors are summed to obtain the total weight of the subsystem. The MTTR budget values ​​of multiple devices are calculated and output based on the total weight of the subsystem, the subsystem MTTR budget value, and the multiple device weighting factors. The multiple devices to be analyzed and the multiple device MTTR budget values ​​are associated to output the structured MTTR allocation table.

[0085] Furthermore, the qualitative design and simulation module 20 is used to perform the following operational steps:

[0086] The maintenance task list is decomposed to obtain an atomic-level operation step sequence; after loading the multi-percentile digital human body model and standard maintenance posture library into the human-machine simulation environment, the digital prototype is imported for scene activation; in the human-machine simulation environment, based on the standard maintenance posture library as a constraint, the multi-percentile digital human body model is driven to perform maintenance process simulation according to the atomic-level operation step sequence, and four-dimensional analysis is performed simultaneously to output the human-machine ergonomics analysis report, the estimated maintenance task time, and the task complexity analysis results.

[0087] Furthermore, the risk assessment and decision-making module 30 is used to perform the following operational steps:

[0088] During the maintenance process simulation, four-dimensional analysis is performed simultaneously, outputting four-dimensional analysis data. This four-dimensional analysis covers attitude feasibility analysis, dynamic interference inspection, visual channel analysis, and maintenance channel analysis. Based on the four-dimensional analysis data and atomic-level operation step sequences, task complexity is assessed, and the task complexity analysis results are output. Attitude comfort data, interference position data, and blind spot data are extracted from the four-dimensional analysis data for comprehensive human-machine interface analysis, and the human-machine interface analysis report is output. The estimated maintenance task time is output by accumulating the maintenance task time during the maintenance simulation process. Based on the task-equipment association, the estimated maintenance task time and the corresponding MTTR budget value of the equipment in the MTTR allocation table are mapped and compared, and a time difference report is output.

[0089] Furthermore, the risk assessment and decision-making module 30 is used to perform the following operational steps:

[0090] Based on the estimated time of the maintenance tasks, maintenance time levels are divided to obtain multiple task time levels for the multiple maintenance tasks; based on the task complexity analysis results, multiple step numbers, multiple fine operation points, and multiple environmental interference items of the multiple maintenance tasks are extracted, and failure probability levels are determined to output multiple task failure probability levels; based on the multiple task time levels and multiple task failure probability levels, RAC values ​​are calculated in the risk assessment matrix, and the multiple risk index RAC values ​​are output.

[0091] Furthermore, if the RAC value of the Kth risk index for the Kth maintenance task does not meet the upper limit of the RAC threshold, closed-loop optimization is triggered until the updated RAC value of the Kth risk meets the upper limit of the RAC threshold.

[0092] Furthermore, closed-loop optimization covers the iterative processes of design optimization loop, maintenance process simulation, and risk assessment.

[0093] Furthermore, the parameter tuning variables of the design optimization loop are the physical structural parameters of the digital prototype, including but not limited to the maintenance hatch diameter, equipment installation angle, pipeline layout topology, connector type, connector marking direction, support structure position, and support structure shape.

[0094] Through the foregoing detailed description of the reliability co-design and evaluation method for reusable rocket maintenance, those skilled in the art can clearly understand the reliability co-design and evaluation system for reusable rocket maintenance in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be found in the method section.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A reliability co-design and evaluation method for reusable rocket maintenance, characterized in that, The method includes: Input the system-level mean time to repair (MTTR), the product function structure tree, and the initial failure rates of multiple devices to be analyzed. Calculate the MTTR allocation table using weighted factors and output the MTTR allocation table. Input the digital prototype, maintenance task list and the MTTR allocation table into the human-machine simulation environment, perform maintenance process simulation, and output human-machine ergonomics analysis report, estimated maintenance task time and task complexity analysis results; Using the estimated time and complexity analysis results of the maintenance tasks as input, multiple risk indices (RAC) values ​​for multiple maintenance tasks are calculated through a risk assessment matrix. The risk levels of the multiple RAC values ​​are classified according to a preset RAC threshold, and multiple task risk levels are output. Based on the multiple RAC values ​​and the multiple task risk levels, the multiple maintenance tasks are prioritized and a maintenance task priority sequence list is output.

2. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 1, characterized in that, The method involves inputting the system-level mean time to repair (MTTR), the product function structure tree, and the initial failure rates of multiple devices to be analyzed. Through recursive calculation using weighted factors, an MTTR allocation table is output. Select the target subsystem based on the product function structure tree, and retrieve the target equipment list from the target subsystem; The initial failure rates of multiple devices to be analyzed in the target device list are summed to obtain the subsystem failure rate. Calculate the subsystem MTTR budget value of the target subsystem based on the overall system failure rate, subsystem failure rate, and the system-level mean time to repair. The weighted allocation module performs recursive calculation of weighted factors based on multiple failure rates of the multiple devices to be analyzed and multiple predefined device attribute factors to obtain multiple device weighted factors. The total weight of the subsystem is obtained by summing the weighting factors of the multiple devices; Based on the total weight of the subsystem, the MTTR budget value of the subsystem, and the weighting factors of multiple devices, the MTTR budget values ​​of multiple devices are calculated and output. Associate the multiple devices to be analyzed with the MTTR budget values ​​of the multiple devices, and output the structured MTTR allocation table.

3. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 1, characterized in that, The method involves inputting the digital prototype, maintenance task list, and MTTR allocation table into a human-machine simulation environment, performing maintenance process simulation, and outputting a human-machine ergonomics analysis report, estimated maintenance task time, and task complexity analysis results. Decompose the maintenance task list to obtain an atomic-level sequence of operation steps; After loading the multi-percentile digital human body model and standard maintenance posture library into the human-computer simulation environment, the digital prototype is imported to activate the scene; In the human-machine simulation environment, based on the standard maintenance posture library as a constraint, and driven by the atomic-level operation step sequence to perform maintenance process simulation of the multi-percentage digital human body model, four-dimensional analysis is performed simultaneously, and the human-machine ergonomics analysis report, maintenance task estimated time, and task complexity analysis results are output.

4. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 3, characterized in that, The method further includes: During the maintenance process, four-dimensional analysis is performed simultaneously in the simulation, and four-dimensional analysis data is output. The four-dimensional analysis covers attitude feasibility analysis, dynamic interference inspection, visual channel analysis, and maintenance channel analysis. Based on the four-dimensional analysis data and the sequence of atomic-level operation steps, the task complexity is evaluated, and the task complexity analysis results are output. From the four-dimensional analysis data, posture comfort data, interference position data, and blind spot data are extracted to perform a comprehensive human-machine ergonomic analysis and output the human-machine ergonomic analysis report. The estimated time for the maintenance task is output by accumulating the maintenance task time during the maintenance process simulation. Based on the task equipment association, the estimated time of the maintenance task is mapped and compared with the MTTR budget value of the corresponding equipment in the MTTR allocation table, and a time difference report is output.

5. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 1, characterized in that, Using the estimated time and complexity analysis results of the maintenance tasks as input, the method further includes calculating multiple risk indices (RAC) values ​​for multiple maintenance tasks through a risk assessment matrix. Based on the estimated time of the maintenance tasks, maintenance time levels are divided to obtain multiple task time levels for the multiple maintenance tasks; Based on the results of the task complexity analysis, the number of steps, fine operation points and environmental interference items of the multiple maintenance tasks are extracted, the failure probability level is determined, and the failure probability level of multiple tasks is output. Based on the multiple task time levels and multiple task failure probability levels, the RAC value is calculated in the risk assessment matrix, and the multiple risk index RAC values ​​are output.

6. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 1, characterized in that, If the RAC value of the Kth risk index for the Kth maintenance task does not meet the upper limit of the RAC threshold, closed-loop optimization is triggered until the updated RAC value of the Kth risk meets the upper limit of the RAC threshold.

7. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 6, characterized in that, The closed-loop optimization covers the iterative process of design optimization cycle, maintenance process simulation and risk assessment.

8. The reliability co-design and evaluation method for reusable rocket maintenance as described in claim 7, characterized in that, The parameter tuning variables of the design optimization loop are the physical structural parameters of the digital prototype, including but not limited to the maintenance hatch diameter, equipment installation angle, pipeline layout topology, connector type, connector marking direction, support structure location, and support structure shape.

9. A reliability co-design and evaluation system for reusable rocket maintenance, characterized in that, The system is used to implement the reliability co-design and evaluation method for reusable rocket maintenance according to any one of claims 1-8, the system comprising: The quantitative index decomposition module 10 is used to input the system-level average repair time, product function structure tree, and multiple initial failure rates of multiple devices to be analyzed. It outputs the MTTR allocation table through weighted factor recursion calculation. The qualitative design and simulation module 20 is used to input the digital prototype, maintenance task list and the MTTR allocation table into the human-machine simulation environment, perform maintenance process simulation, and output human-machine ergonomics analysis report, estimated maintenance task time and task complexity analysis results. The risk assessment and decision-making module 30 is used to take the estimated time of the maintenance task and the result of the task complexity analysis as input, calculate multiple risk index (RAC) values ​​of multiple maintenance tasks through a risk assessment matrix, classify the risk of the multiple risk index RAC values ​​according to a preset RAC threshold, output multiple task risk levels, prioritize the multiple maintenance tasks according to the multiple risk index RAC values ​​and multiple task risk levels, and output a maintenance task priority sequence list.

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