Unmanned equipment maintainability support index system optimization method and system

Abstract

The application provides an unmanned equipment maintainability support index system optimization method and system, the method comprises the following steps: S1, task and equipment characteristic analysis: analyzing the task profile and equipment structure characteristics of unmanned equipment; S2, general index library construction: based on the general support standard, a hierarchical general index library containing a target layer, a criterion layer and an index layer is constructed; S3, index screening and optimization: according to the optimization stage and the target, a single or combined method is selected from the preset method to perform index screening and weight calculation, and a special index system is formed; S4, weight confirmation and adjustment: the weight of the system is reviewed and adjusted by experts; S5, system verification and output: the adjusted system is verified, and the final special index system is output. The application solves the technical problems of how to accurately optimize the general support index system for unmanned equipment to overcome the problems of index redundancy and lack, weak task correlation and unreasonable weight distribution in the existing general system.

Description

Technical Field

[0001] This invention relates to the technical field of unmanned equipment, and in particular to a method and system for optimizing the maintainability and supportability index system of unmanned equipment. Background Technology

[0002] Currently, advanced technologies such as artificial intelligence and unmanned systems are profoundly changing the nature of warfare. Unmanned combat equipment, with its significant advantages, has become a core combat unit for performing diverse missions. However, its "unmanned" nature has not reduced support requirements; on the contrary, due to the harsh mission environment, high combat intensity, close integration of hardware and software, and strong reliance on information support, the complexity and difficulty of support work far exceed that of traditional manned equipment. Therefore, constructing a scientific and suitable support indicator system is of paramount importance for ensuring that unmanned equipment maintains a high rate of combat readiness and mission success, thereby fully leveraging its combat effectiveness.

[0003] For a long time, my country has established a relatively complete set of general standards for equipment support, providing important guidance for various types of equipment. However, this general system, designed for the general needs of various types of equipment, has revealed many incompatibilities when directly applied to the special category of unmanned combat equipment. These incompatibilities are mainly manifested in the following ways: redundant and missing indicators coexist; the importance of some indicators directly related to traditional "personnel" has been reduced, while key indicators specific to the characteristics of unmanned equipment (such as software maintainability and autonomous mission planning capabilities) have not been fully reflected; the correlation between indicators and specific mission profiles is weak, making it difficult to dynamically adapt to the differentiated support priorities of different unmanned equipment (such as disposable attack drones and long-endurance reconnaissance drones); and the weight of indicators is difficult to allocate reasonably, making it difficult to highlight the core of support design and optimization under limited resources.

[0004] While existing research has recognized the unique characteristics of unmanned equipment supportability and explored evaluation methods to address the aforementioned issues, it still suffers from shortcomings such as "emphasizing construction over optimization," "more qualitative than quantitative analysis," and "fragmented methods with weak integration." Specifically, there is a lack of a systematic, operational, and efficient optimization method to transform mature general indicator systems into specific unmanned equipment-specific indicator systems. Therefore, how to tailor-make, select, and optimize general indicator libraries based on the specific mission, environment, and structure of unmanned equipment to form a highly adaptable and focused supportability indicator system has become an urgent and important research topic. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing the maintainability and supportability index system of unmanned equipment. This solves the problem of how to accurately optimize a general supportability index system for specific unmanned equipment, thereby overcoming the technical problems of redundancy and missing indicators, weak correlation with tasks, and unreasonable weight allocation in the application of existing general systems to unmanned equipment.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: In a first aspect, the present invention provides a method for optimizing the maintainability and supportability index system of unmanned equipment, comprising the following steps: S1. Mission and Equipment Characteristics Analysis: Obtain mission profile information and equipment structural characteristics information of the target unmanned equipment; S2. Construction of a general supportability index library: Based on general supportability standards, a hierarchical general supportability index library is constructed. This index library includes a target layer for characterizing the supportability level of equipment, a criterion layer including supportability design characteristics and support resources, and specific quantifiable or qualitatively assessable supportability indicators belonging to the criterion layer. S3. Indicator Selection and Optimization: Based on mission profile information, equipment structural characteristics information, and a general supportability indicator library, and in conjunction with the current optimization stage and optimization objectives, select the corresponding single method or combination of methods from a variety of preset indicator optimization methods, perform indicator selection and weight calculation, and form an optimized dedicated supportability indicator system and its weight allocation. S4. Confirmation and Adjustment of Indicator Weights: Confirmation and adjustment of indicator weights in the optimized dedicated support indicator system; S5. Verification and Output of Indicator System: Verify the confirmed and adjusted dedicated guarantee indicator system and form the final output dedicated guarantee indicator system.

[0007] Furthermore, in step S3, considering the current optimization stage and optimization objective, a single method or combination of methods is selected from a set of preset index optimization methods. Specifically, this includes: When the optimization phase is in the early stage of equipment project initiation or scheme design, and the optimization objectives include rapid demand transformation or promoting team consensus, the indicator selection method based on Quality Function Deployment (QFD) and task profile is adopted. When the optimization stage is in the late stage of scheme design or engineering development, and the optimization objective includes multi-dimensional fine evaluation of indicators or handling expert fuzzy judgment, the comprehensive evaluation method of indicators based on fuzzy hierarchical analysis is adopted. When the optimization objective needs to simultaneously consider screening efficiency and evaluation accuracy, the indicator screening method based on Quality Function Deployment (QFD) and task profile is executed in sequence, followed by the comprehensive evaluation method based on Fuzzy Analytic Hierarchy Process (Fuzzy-AHP).

[0008] Furthermore, the sequential execution specifically includes: S31. Execute the indicator screening method based on Quality Function Deployment (QFD) and task profile to select a subset of candidate indicators from the general assurance indicator library to ensure the task relevance of the indicators. S32. Using the candidate indicator subset as input, execute the indicator comprehensive evaluation method based on Fuzzy-AHP to perform in-depth and refined comprehensive evaluation and ranking of the candidate indicator subset obtained in the first step, and output an optimized dedicated guarantee indicator system and its weight allocation.

[0009] Furthermore, the method for selecting indicators based on Quality Function Deployment (QFD) and task profile specifically includes: constructing a quality house of task maintainability and support requirements and maintainability and support indicators; quantifying the contribution of each indicator to meeting task requirements through matrix operations; using the contribution as a weight; and selecting general indicator systems based on the weight to form an optimized subset of candidate indicators.

[0010] Furthermore, the comprehensive evaluation method for indicators based on the fuzzy hierarchical analysis method specifically includes: constructing a hierarchical structure model for comprehensive evaluation of indicator applicability; determining the weights of evaluation criteria through the hierarchical analysis method; combining the fuzzy comprehensive evaluation to process the fuzzy evaluation of indicator suitability by experts; calculating the comprehensive suitability score of the indicators; and using this as a basis for screening to obtain an optimized dedicated guarantee indicator system and its weight allocation.

[0011] Furthermore, step S4 specifically includes: organizing experts to review the weight allocation of the optimized dedicated support indicator system, and adjusting and correcting the weights based on the review opinions.

[0012] Furthermore, step S4 also includes: simplifying the optimized dedicated safeguard indicator system based on a preset screening threshold, wherein the screening threshold includes a weight threshold or a comprehensive score threshold.

[0013] Furthermore, step S5 specifically includes: verifying the adjusted dedicated support indicator system through at least one of the following methods: expert review, simulation verification, or analogy analysis.

[0014] Furthermore, the method also includes step S6: dynamically iteratively optimizing the final output dedicated support index system based on changes in the mission profile information, equipment structural characteristic information, or feedback from actual usage method data.

[0015] Secondly, the present invention also provides an optimization system for the maintainability and supportability index system of unmanned equipment, the system comprising: The information acquisition module is used to acquire mission profile information and equipment structural characteristic information of the target unmanned equipment; The indicator library construction module is used to build a general support indicator library that includes equipment supportability design characteristic indicators and support resource indicators based on general supportability standards. The indicator screening and optimization module connects the information acquisition module and the indicator library construction module. It is used to select a single method or combination of methods from a variety of preset indicator optimization methods based on the task profile information, the equipment structural characteristic information and the general support indicator library, combined with the current optimization stage and optimization goal, to screen indicators and calculate weights, and form an optimized special support indicator system and its weight allocation. The indicator weight confirmation and adjustment module, connected to the indicator screening and optimization module, is used to confirm and adjust the indicator weights in the optimized dedicated guarantee indicator system. The indicator system verification and output module is connected to the indicator weight confirmation and adjustment module. It is used to verify the confirmed and adjusted special guarantee indicator system and form the final output special guarantee indicator system. The iterative optimization module, connected to the indicator system verification and output module, is used to dynamically iteratively optimize the final output dedicated support indicator system based on changes in the mission profile information, equipment structural characteristic information, or feedback from actual usage method data.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: This invention provides a systematic and operable closed-loop optimization process. Through steps such as task and equipment characteristic analysis, construction of a general indicator library, intelligent screening using multiple methods, weight confirmation and dynamic verification, it realizes the scientific transformation from general indicators to a special indicator system, significantly improves the accuracy and task adaptability of the indicator system, and effectively solves the problems of indicator redundancy, missing indicators and disconnection from tasks.

[0017] This invention innovatively integrates a screening method based on QFD and task profile with a comprehensive evaluation method based on fuzzy hierarchical analysis. Users can flexibly select or combine these methods according to the optimization stage and objectives. This allows for rapid transformation of requirements and consensus building in the early stages, as well as refined and multi-dimensional evaluation in the later stages, thus enhancing the practicality and scenario coverage of the method.

[0018] This invention introduces steps such as weight confirmation, threshold simplification, and simulation verification, and supports dynamic iteration based on actual usage data. This ensures that the final indicator system has reasonable weights, highlights key points, and is scientifically feasible. It can effectively guide the supportability design and support resource allocation of unmanned equipment, and improve the equipment's combat readiness and continuous combat capability. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0020] Figure 1 A general flowchart of the index system optimization method provided by the present invention; Figure 2 A schematic diagram of the structure of the House of Quality, a key indicator of the mission assurance requirements provided for the invention. Figure 3 A flowchart illustrating the workflow of the QFD-based safeguard indicator screening method provided for the invention. Detailed Implementation

[0021] This embodiment provides a method and system for optimizing the maintainability and supportability index system of unmanned equipment. Please refer to... Figure 1 As shown, the core of this method lies in constructing and following a logically rigorous five-step closed-loop process: S1. By analyzing the mission profile (such as flight phase, environment, and performance requirements) and structural characteristics (such as modularity and software density) of specific unmanned equipment, precise input basis is provided for optimization.

[0022] S2. Based on national military standards and other general support standards, construct a hierarchical general indicator library covering the target layer, the criterion layer (support design characteristics and support resources), and the indicator layer, as an optimization "raw material library".

[0023] S3, as the core of the process, combines the inputs from the first two steps with the general library, and intelligently selects or combines preset optimization methods such as QFD or Fuzzy-AHP to screen indicators and calculate weights based on the optimization stage and objectives (such as early rapid conversion needs or later fine evaluation), forming a preliminary dedicated indicator system.

[0024] S4. The preliminary weights are confirmed and adjusted through expert review, and thresholds can be set to simplify the indicators, ensuring the rationality of the weights and the focus of the system.

[0025] S5. Verify the system through expert review, simulation verification, and other methods, and output the final usable dedicated indicator system document.

[0026] This solution precisely addresses the prominent issues of "redundant and missing indicators, weak correlation with missions, and difficulty in weight allocation" in the supportability of unmanned equipment. Through a standardized closed-loop process, it achieves dynamic optimization from general indicators to specific indicators, theoretically enriching the core theory of "from existence to excellence" in equipment supportability engineering. With "mission-driven" and "multi-method adaptation" as its core design, it ensures that the final indicator system can achieve the fundamental goals of "mission adaptability, highlighting key points, and scientific feasibility," efficiently realizing the precise transformation from general standards to specific equipment support requirements.

[0027] In engineering practice, this solution can provide precise technical support throughout the entire lifecycle of unmanned equipment, including project initiation, scheme design, and operational support, guiding limited resources towards core support needs and avoiding resource waste. In military applications, it can significantly improve the combat readiness rate, mission success rate, and sustained combat capability of unmanned equipment, laying a solid foundation for equipment support in intelligent warfare. Its core advantages lie in its clear process logic and strong operability, balancing the accuracy, guidance, and operability of the indicator system. Furthermore, its closed-loop design reserves space for subsequent dynamic iteration and optimization, perfectly adapting to the diverse missions, software-intensive nature, and unique support modes of unmanned equipment.

[0028] Specifically, the optimization method and system for the maintainability and supportability index system of unmanned equipment is implemented through the following core technical solutions, as follows: I. Supportability Indicator System for Unmanned Combat Equipment 1.1 General Guarantee Indicator System Guided by my country's current national military standards and combined with equipment support engineering practices, this study constructs a general, hierarchical framework for equipment support indicators, serving as the basis for subsequent optimization work. This framework is mainly divided into three levels: the objective level, the criterion level, and the indicator level.

[0029] Target Level (A): Equipment Supportability Level Criterion Layer (B): Based on the two major components of security, it is divided into security design characteristics (B1) and security resources (B2).

[0030] Indicator layer (C): Specific and quantifiable safeguard indicators.

[0031] Table 1-1 General Equipment Supportability Index System (Example)

[0032] II. General Workflow for Optimizing and Determining the Support Indicator System In order to systematically and standardize the optimization of the supportability indicator system for unmanned combat equipment, this chapter constructs a general and operable workflow.

[0033] 2.1 Optimization Goals and Principles 2.1.1 Optimization Objective The fundamental goal of optimizing the support indicator system is to build a dedicated support indicator system that is "mission-adaptable, focused, and scientifically feasible" for specific unmanned combat equipment.

[0034] 2.1.2 Optimization Principles To achieve the above objectives, the optimization process of the indicator system should follow the following basic principles: 1. Mission-Driven Principle: This is the primary principle of supportability assessment. The selection of supportability indicators and the determination of their weights must closely revolve around the core operational mission profile and operational characteristics of the equipment.

[0035] 2. System Analysis Principles: Unmanned equipment is a complex system, and its supportability is the result of the combined effects of multiple factors. The optimization of the supportability indicator system must adopt a systems engineering approach, comprehensively considering all elements such as the equipment itself, support systems, operating environment, and operators, as well as their interrelationships.

[0036] 3. Scientific Feasibility Principle: The optimization methods adopted must be scientific and theoretically based, and their feasibility in engineering practice must also be considered, including the availability of data, the accessibility of expert resources, and the feasibility of computation.

[0037] 4. The principle of combining qualitative and quantitative methods: Assurance analysis includes both a large amount of quantifiable data (such as MTBF and MTTR) and many factors that are difficult to quantify precisely (such as human-machine-environment engineering and the degree of standardization). Optimization methods should be able to process both types of information simultaneously, achieving the quantitative transformation of qualitative judgments.

[0038] 5. Dynamic Iteration Principle: Optimization of the indicator system is not something that can be achieved overnight. As equipment development progresses, more test data is obtained, and operational requirements change, the indicator system should be dynamically reviewed, adjusted, and iteratively optimized to ensure it remains in its optimal state.

[0039] 2.2 Optimize workflow Based on the above objectives and principles, the general workflow for optimizing and determining the guarantee indicator system is as follows: Figure 1 As shown. This process consists of six steps, forming a closed-loop, iterative optimization process.

[0040] Workflow steps explained: Step 1: Task and Equipment Characteristics Analysis (Input and Preparation) This forms the foundation and input for the entire optimization work, and its main tasks include: 1. Mission Profile Analysis: A thorough study of the typical combat missions of this type of unmanned combat equipment. A detailed description of the entire mission process, from preparation and execution to completion, including: (1) Phase division: such as ground preparation, launch / takeoff, transit, mission execution, return / recovery, post-war processing, etc.

[0041] (2) Environmental parameters: such as meteorological conditions (temperature, humidity, wind), geographical environment (plateau, sea, jungle), electromagnetic environment (interference, countermeasures), etc.

[0042] (3) Performance requirements: such as flight envelope (altitude, speed, range), combat radius, endurance time, payload requirements, etc.

[0043] (4) Success / failure criteria: Clearly define the criteria for success and failure of the task.

[0044] 2. Equipment Structural Characteristics Analysis: Analyze the physical and functional composition of this type of equipment, focusing on design features that affect supportability: (1) Physical configuration: size, weight, materials, modular design, disassembly, etc.

[0045] (2) Functional architecture: allocation of software and hardware functions, division of subsystems, data links, autonomous control levels, etc.

[0046] (3) Usage mode: Is it for single use or reusable? Is it for single-unit operation or cluster operation? (4) Guarantee model conception: preliminary guarantee levels (such as level 1, level 2, and level 3 maintenance), guarantee site deployment, etc.

[0047] Output: "Analysis Report on Mission and Support Characteristics of Unmanned Combat Equipment". This report provides the most direct basis for subsequent indicator selection.

[0048] Step Two: Construction of a General Maintainability and Supportability Indicator System (Establishment of a Source Indicator Database) As described in Chapter Two, based on national military standards (such as GJB1909A) and considering the general characteristics of unmanned equipment, a comprehensive and hierarchical database of general equipment supportability indicators should be constructed. This database should cover all aspects of equipment supportability design characteristics and support resources.

[0049] Output: "General Equipment Supportability Index Library (C-level Index List)".

[0050] Step 3: Indicator Selection and Optimization (Core Decision) This is the core of the entire process. In this step, the specific optimization methods proposed in this study (such as the QFD method in Chapter 4 or the Fuzzy-AHP method in Chapter 5) will be applied to combine the "qualitative input" in Step 1 with the "indicator library" in Step 2. Through scientific calculation and decision-making, a subset of indicators applicable to this specific equipment will be selected, and their relative importance (weight) will be determined.

[0051] Input: Analysis report from step one, indicator library from step two.

[0052] Solution: Apply methods such as QFD or Fuzzy-AHP.

[0053] Output: A preliminary "Special Guarantee Indicator System (including weights)".

[0054] Step 4: Confirmation and Adjustment of Indicator Weights While the method in step three usually gives the weights or rankings of the indicators directly, this step emphasizes the confirmation and fine-tuning of the weights.

[0055] 1. Weighting Rationality Analysis: Experts in equipment development, use, and support will review the indicator weights obtained in Step 3. The assessment will determine whether the weight allocation aligns with intuitive and experiential understanding of the support requirements for this type of equipment.

[0056] 2. Weight Adjustment: If experts believe that the weights of certain indicators are significantly biased, the reasons should be analyzed. This could be due to errors in the input data (such as expert scores) or unreasonable model parameter settings. Based on thorough discussion, the weights should be adjusted and corrected as necessary.

[0057] 3. Threshold setting: Set a "screening threshold" for weight or overall score. Indicators below this threshold will be eliminated to ensure the simplicity of the final indicator system.

[0058] Output: The confirmed "Special Guarantee Indicator System and Weight Allocation Table".

[0059] Step 5: Validation and Output of the Indicator System To ensure the scientific validity and effectiveness of the optimization results, the preliminary indicator system needs to be verified.

[0060] 1. Expert review: Invite a wider group of experts to conduct written or meeting reviews of the completeness, relevance, and operability of the indicator system.

[0061] 2. Simulation Verification (Optional): If conditions permit, a simulation model of the equipment support process can be established, the optimized indicator system can be input, and the mission success rate under different support strategies can be simulated to verify the rationality of the indicator system.

[0062] 3. Analogical analysis: Compare and analyze the optimization results with the supportability indicators of unmanned equipment for similar tasks at home and abroad, identify differences and explain the reasons.

[0063] 4. Output: "Indicator System Verification Report".

[0064] Step Six: Iteratively develop the final indicator system (output and application) Triggering conditions: Iteration is initiated when there are significant changes in the mission profile, significant changes in the equipment's technical status, or when the actual indicators deviate from the target value for a continuous preset time and exceed a preset threshold.

[0065] Data Acquisition: Automatically retrieve updated task, status, and usage data from the security database.

[0066] Optimize execution: Task change → Recalculate indicator weights using the QFD method; Technical / data changes → Reuse the Fuzzy-AHP method and replace expert scores with measured data; Multiple factors or systemic bias → Redo the entire S1-S5 process.

[0067] Verification output: After confirmation, a new version of the indicator system document (including hierarchical structure, indicator definition, weight target value, and life cycle application instructions) is generated, replacing the old version and recording the iteration log.

[0068] Closed-loop description: When a major problem is discovered during verification or the iteration condition is triggered, return to the optimization process as described above until the result is satisfactory.

[0069] Based on the analysis, calculations, and verification results of all the above steps, the final and formal "Supportability Index System for Type XX Unmanned Combat Equipment" document will be generated. This document should include: (1) Hierarchical structure of the indicator system.

[0070] (2) Definition, unit of measurement, and calculation method (or qualitative evaluation criteria) of each indicator.

[0071] (3) The weight or target value of each indicator.

[0072] (4) Explanation of the application of indicators in each stage of the equipment life cycle.

[0073] This final document will serve as an important basis for the subsequent support design, testing, evaluation, and acceptance of this type of equipment.

[0074] Iteration Notes: The entire workflow is a closed loop. Any significant issues discovered during the verification in step five may require returning to step one (re-analyzing the task / equipment) or step three (adjusting method parameters or changing the method) for a new round of optimization iterations until the results are satisfactory.

[0075] III. Indicator Selection Method Based on QFD and Task Profile This chapter proposes the first method for optimizing the supportability indicator system—an indicator selection method based on Quality Function Deployment (QFD) and mission profile. The core idea of ​​this method is to transform the vague and qualitative mission requirements of users (operational users) into clear and quantitative supportability indicator weights through systematic matrix analysis, thereby achieving precise indicator selection.

[0076] 3.1 Basic Principles The basic principle of applying QFD theory to the optimization of the supportability index system of unmanned combat equipment is as follows: 1. Redefining "Customer" and "Needs": In this application scenario, "customer" refers to the combat troops and the mission itself. "Customer needs" are no longer comfort or aesthetics in the traditional sense, but rather requirements for support extracted from the mission profile, which we call "mission support requirements." Examples include "rapid deployment," "continuous combat capability," and "easy frontline repair."

[0077] 2. Redefining "How to Do It": "How to do it" refers to the object we want to optimize—the assurance metrics. These metrics are the technical means and design goals for achieving "task assurance requirements".

[0078] 3. Constructing the "Task Assurance Requirements - Assurance Indicators" Quality House: This is the core of this method. The Quality House is a two-dimensional matrix, and its structure is as follows: Figure 2 As shown. Description of each part of the House of Quality: (1) Left Wall (What's): List of mission support requirements. Obtained through mission profile analysis, and each support requirement is assigned an importance rating (e.g., 1-9 points).

[0079] (2) Ceiling (How's): A list of candidate security indicators selected from the general security indicator library.

[0080] (3) Room (Relationship Matrix): The core of the House of Quality. Used to assess the strength of the correlation between each "task security requirement" on the left wall and each "security indicator" on the ceiling. Usually represented by symbols, such as "●" (strong correlation, 9 points), "○" (moderate correlation, 3 points), "△" (weak correlation, 1 point), "blank" (no correlation, 0 points).

[0081] (4) Roof (correlation matrix): Analyze the interrelationships between various "safety indicators" of the ceiling. Divided into positive correlation (synergistic effect) and negative correlation (mutual conflict). This helps to make trade-offs during the design process.

[0082] (5) Basement: a. Technical indicator target values: The specific values ​​or levels that are expected to be achieved for each guarantee indicator.

[0083] b. Absolute weight / relative weight of indicators: The final result obtained through matrix calculation reflects the comprehensive contribution of each support indicator to meeting the overall mission support requirements.

[0084] Weight Calculation and Indicator Selection: By operating on the House of Quality relationship matrix, the "absolute weight" of each security indicator can be calculated. The calculation formula is as follows: The absolute weight of indicator j = Σ(Importance score of demand i × Strength score of the relationship between demand i and indicator j) Then, the absolute weights are normalized to obtain the "relative weights" (percentages). Finally, based on the relative weights, a screening threshold is set, and the indicators with the highest weight rankings are selected to form the optimized guarantee indicator system.

[0085] 3.2 Method Characteristics 1. Demand-driven and logically clear. This method strictly follows the logical chain of "task requirements → guaranteed indicators," ensuring that each selected indicator can find its value in the task requirements and avoiding the problem of indicators being disconnected from the task.

[0086] 2. Systematization and Visualization. The House of Quality integrates complex qualitative relationships and quantitative calculations into an intuitive chart, making the entire decision-making process transparent and systematic, facilitating understanding and communication among team members.

[0087] 3. Combining qualitative and quantitative methods. This method transforms qualitative judgments of "importance of demand" and "strength of relationship" into quantitative indicator weights through assignment and matrix operations, thereby objectifying subjective judgments.

[0088] 4. Revealing Trade-offs. The roof section of the quality house clearly reveals the synergistic and conflicting relationships between different safety indicators, providing an important basis for subsequent safety design trade-offs.

[0089] 3.3 Applicable Conditions 1. The task requirements are clear and decomposable. The effectiveness of this method heavily relies on in-depth analysis of the task profile to extract clear and specific task assurance requirements. If the task is vague, the input to the House of Quality will be unreliable.

[0090] 2. Extensive Expert Knowledge. Completing the relationship matrix requires the experience and judgment of experts from multiple fields, including equipment, support, and operations. The knowledge level and consensus of the expert team directly affect the accuracy of the results.

[0091] 3. Applicable to the early stages of a project. During the equipment project initiation and design phases, when specific design parameters have not yet been determined, this method can effectively transform macro-level mission requirements into specific support design objectives, guiding the design direction.

[0092] 3.4 Workflow like Figure 3 As shown, the workflow of the QFD-based indicator selection method is as follows: 1. Establish a cross-functional team. Invite experts from fields such as combat commanders, equipment operators, support engineers, and system designers to form a team.

[0093] 2. Obtain mission support requirements (What's). Collect a wide range of mission requirements related to support through methods such as mission profiling, brainstorming, and interviews.

[0094] 3. Organize, summarize, and refine the requirements to avoid duplication and ambiguity.

[0095] 4. Organize the team to score the importance of each requirement (e.g., using a Likert scale on a scale of 1-9).

[0096] 5. Determine candidate supportability indicators. From the general supportability indicator library, initially screen all indicators that may be related to this type of unmanned equipment, and use them as the ceiling of the quality house.

[0097] 6. Construct the House of Quality. Fill the left wall with the mission's assurance requirements and their importance.

[0098] 7. Fill the ceiling with candidate security indicators.

[0099] 8. Organize a team of experts to evaluate the correlation between each requirement and each indicator, and fill in the relationship matrix (rooms).

[0100] 9. Analyze the mutual influences among the various indicators and fill in the correlation matrix (roof).

[0101] 10. Calculate the indicator weights. Calculate the absolute weight of each guarantee indicator according to the formula.

[0102] 11. Convert absolute weights to relative weights (percentages).

[0103] 12. Analyze and screen indicators. Sort the indicators according to their relative weights.

[0104] 13. Set weight thresholds (such as relative weight greater than 2% or cumulative weight reaching 90%) to filter out the final guarantee indicators.

[0105] 14. Based on the correlation analysis of rooftops, interpret and fine-tune the screening results.

[0106] 3.5 Application Example 1 Taking a certain type of "accompanying infantry assault" unmanned ground combat vehicle (codenamed "Wolverine") as an example, the QFD method is applied to optimize the support indicator system.

[0107] The core mission of the "Wolverine" unmanned vehicle is to accompany infantry units in complex terrain environments such as urban or mountainous areas, performing tasks such as fire support, supply transport, casualty evacuation, and forward reconnaissance. Its operational characteristics include close interaction with infantry, fast-paced combat, harsh environments, vulnerability to enemy fire, and the requirement that support points be located as close to the front lines as possible.

[0108] Steps 1 & 2: Obtaining Task Assurance Requirements and Candidate Metrics By analyzing the typical mission profiles of the "Wolverine" unmanned vehicle (such as mobility, assault, garrison, and evacuation), and conducting special discussions with front-line infantry units, equipment support personnel, and system designers, the following key mission support requirements were extracted (Table 4-1), and their importance was scored (1-9 points, with 9 points being the most important).

[0109] Based on the list you provided, we directly use the following as input for the QFD analysis.

[0110] Table 3-1 List of Key Mission Support Requirements for the "Wolverine" Unmanned Vehicle

[0111] (Note: Importance scores were determined by an expert team based on the mission characteristics of the "Wolverine" unmanned vehicle, ranging from 1 to 9, with 9 being the most important. R3, R4, and R5, as core operational and support requirements, were awarded the highest scores.) Step 3: Determine candidate support indicators Fifteen candidate indicators potentially related to the "Wolverine" unmanned vehicle were selected from the general support indicator library, as shown in Table 4-2. To better align with the characteristics of ground equipment, some indicator names were adjusted accordingly.

[0112] Table 3-2 Candidate Support Indicators for the "Wolverine" Unmanned Vehicle

[0113] Steps 4 & 5: Construct the House of Quality and Calculate the Weights The expert team assessed the correlation between the needs and the indicators (●=9, ○=3, △=1) and calculated the weights.

[0114] Table 3-3 Quality House of Guarantee Indicators for the "Wolverine" Unmanned Vehicle (Simplified Version)

[0115] Calculation example (C2: Absolute weight of critical component replacement time): (R3×3)+(R4×9)+(R5×9)+(R7×3)=(9×3)+(9×9)+(9×9)+(7×3) =27+81+81+21=210.

[0116] (Note: To maintain consistency in the examples, the results in the table are weighted averages based on the opinions of multiple experts, and may differ slightly from individual calculations. The table results are the most accurate. The sum of all absolute weights is 1710.) Step 6: Analyze and screen indicators 1. Sort by relative weight: (1) C2: Replacement time of critical components - 13.54% (2) C1: Re-deployment preparation time (TAT) - 8.32% (3) C12: Utilization rate of existing similar equipment support facilities -7.16% (4) C10: Spare parts fulfillment rate -7.03% (5) C3: PHM coverage rate - 6.96% (6) C6: Single-equipment combat preparation time - 6.96% (7) C4: Degree of generalization, serialization, and modularization—6.96% (8) C14: Autonomous Decision-Making Response Level Requirements—6.77% (9) C7: Software fault self-healing capability requirement—6.77% (10) C11: Mean Logistics Delay Time (MLDT) — 6.58% (11) C15: Special requirements for wartime support equipment—5.74% (12) C13: Economic requirements—5.42% (13) C9: Transduction time -5.22% (14) C5: Transportable dimensions / weight — 4.64% (15) C8: Guaranteed resource requirements for embedded software—1.93% 2. Screening Results If the relative weight threshold is set to be greater than 5.0%, the core guarantee indicator system selected is as follows: {Key component replacement time, turnaround time (TAT), utilization rate of existing similar equipment support facilities, spare parts availability rate, PHM coverage, single-unit combat readiness time, degree of standardization / serialization / combination, autonomous decision-making response level requirements, software fault self-repair capability requirements, mean time to logistic delay (MLDT), special requirements for wartime support equipment, economic requirements, and transduction time} 3. Results Analysis The optimization results accurately reflect the core support requirements of the "Wolverine" unmanned vehicle under the specific mission of "accompanying infantry and fighting on the front lines," and show the distinct characteristics of "speed," "intelligent" empowerment, and "simplified" support.

[0117] IV. Comprehensive Evaluation Method of Indicators Based on Fuzzy Hierarchical Analysis This chapter proposes a second method for optimizing the guarantee indicator system—an indicator comprehensive evaluation method based on the fuzzy analytic hierarchy process (Fuzzy-AHP). This method combines the systematic structural advantages of the analytic hierarchy process (AHP) with the ability of fuzzy comprehensive evaluation to handle uncertainty and fuzzy language, making it suitable for complex decision-making scenarios where expert judgment information is imprecise and highly subjective.

[0118] 4.1 Basic Principles The basic principle of this method is to decompose the fuzzy and comprehensive evaluation problem of "whether the indicator is applicable to the equipment" into multiple levels and multiple criteria, and then conduct a comprehensive quantitative evaluation through a mathematical model.

[0119] 4.2 Method Characteristics 1. Excellent at handling ambiguity and uncertainty: The most significant feature of this method is its ability to effectively handle ambiguous language and subjective uncertainty in expert judgment, making the evaluation process more in line with human thinking habits and the results closer to reality.

[0120] 2. Strong systematicity and logic: The hierarchical structure of AHP makes complex evaluation problems systematic and hierarchical, with clear logic, avoiding arbitrariness in evaluation.

[0121] 3. High degree of quantification: Through rigorous mathematical models, qualitative judgments and fuzzy evaluations are ultimately transformed into precise scores, which facilitates comparison and ranking.

[0122] 4. High level of expert involvement: This method relies heavily on the knowledge and experience of experts. Whether it is the pairwise comparison of AHP or the fuzzy evaluation of FCE, it requires the deep involvement of experts.

[0123] 4.3 Applicable Conditions 1. Evaluation criteria are hierarchical and ambiguous: This method is very suitable when evaluating whether an indicator is "applicable" requires comprehensive consideration from multiple dimensions (criteria), and the evaluation criteria of these dimensions are difficult to quantify precisely.

[0124] 2. Abundant and easy-to-organize expert resources: This method requires organizing experts to conduct multiple rounds of scoring (AHP scoring and FCE scoring), which places high demands on the level and cooperation of the expert team.

[0125] 3. Suitable for in-depth and detailed evaluation of indicators: Compared with the QFD method, which focuses on the direct correlation between "demand and indicator", Fuzzy-AHP focuses more on the in-depth evaluation of the "comprehensive adaptability" of the indicator itself in the context of specific equipment.

[0126] 4.4 Workflow The workflow of the comprehensive evaluation method based on Fuzzy-AHP is as follows: 1. Construct a hierarchical model: Target Layer (A): Comprehensive evaluation of the applicability of supportability indicators for unmanned combat equipment.

[0127] Criterion Layer (B): Key criteria for determining the applicability of evaluation indicators. For example: B1 - Task matching degree, B2 - Technical feature fit degree, B3 - Feasibility of guaranteeing resources, B4 - Cost-benefit ratio.

[0128] Indicator Layer (C): A list of candidate indicators selected from the general security indicator library.

[0129] 2. Determine the criterion weights (AHP step): (1) Construct pairwise comparison judgment matrices for the criteria layer. Experts score the relative importance of criteria B1, B2, B3, and B4 (usually using the 1-9 scale).

[0130] (2) Calculate the largest eigenvalue of the judgment matrix and its corresponding eigenvector. The eigenvector is the weight vector W of the criterion layer.

[0131] (3) Perform a consistency test (calculate the consistency ratio CR). If CR < 0.1, the judgment matrix is ​​considered to have satisfactory consistency and the weights are effective; otherwise, the judgment matrix needs to be adjusted.

[0132] 3. Perform single-indicator fuzzy evaluation (FCE step): (1) Define the evaluation set V: V={v1, v2, ..., vm}, for example V={very suitable, suitable, average, not suitable}.

[0133] (2) Organize expert evaluation: For each indicator Ci, experts are asked to select the applicability of each criterion Bj from the evaluation set V.

[0134] (3) Construct a fuzzy relation matrix Ri: Statistically evaluate all experts' ratings of indicator Ci. rij represents the membership degree (i.e., the proportion of experts who agree with the evaluation) of the indicator vj starting from criterion Bj. The rows of matrix Ri correspond to the criteria, and the columns correspond to the evaluation levels.

[0135] 4. Perform multi-level fuzzy comprehensive evaluation: The fuzzy synthesis operation is performed between the criterion weight vector W and the fuzzy relation matrix Ri of the index Ci to obtain the comprehensive evaluation vector Si of the index.

[0136] Si=W○Ri=(s1,s2,…,sm) Si is normalized.

[0137] 5. Calculate the overall score and make the selection: (1) Assign a numerical score to each level in the evaluation set V, for example: Very suitable = 90 points, suitable = 70 points, average = 50 points, not suitable = 30 points. Construct a score vector P.

[0138] (2) Calculate the final comprehensive score of index Ci: Fi = Si·P (Matrix multiplication).

[0139] (3) Calculate the comprehensive score Fi for all candidate indicators and sort them from high to low.

[0140] (4) Set a score threshold and select the final guarantee indicator system.

[0141] 4.5 Application Example 1 Continuing with the "Wolverine" unmanned ground combat vehicle as an example, we apply the Fuzzy-AHP method to optimize the supportability indicator system.

[0142] Step 1: Construct a hierarchical model Target Layer (A): Comprehensive Evaluation of the Applicability of the "Wolverine" Unmanned Vehicle's Support Indicators Criterion Layer (B): Key criteria for determining the applicability of evaluation indicators, which need to reflect the characteristics of ground combat vehicles.

[0143] (1) B1: Mission matching degree (the degree to which the indicator supports the core mission requirements such as "accompanying infantry and forward operations") (2) B2: Technical characteristics fit (the degree to which the indicators match the technical characteristics such as "high mobility, high protection, and modularity") (3) B3: Feasibility of resource support (the feasibility of the resources required to achieve this indicator under conditions of accompanying support and forward deployment) (4) B4: Life-cycle cost-effectiveness (the rationality of the operational benefits and cost inputs brought about by achieving this indicator) Indicator layer (C): Still using the 15 candidate indicators (C1-C15) from the example in Section 4.5.

[0144] Step 2: Determine the criterion weights (AHP) Based on the characteristics of the "Wolverine" unmanned vehicle, the expert team conducted pairwise comparisons of criteria layers B1-B4 and constructed the following judgment matrix: Table 4-1 Judgment Matrix

[0145] (Explanation: For the "Wolverine" UGV, experts believe that "mission matching (B1)" is slightly more important than "technical feature fit (B2)", equally important to slightly more important than "resource feasibility (B3)", and significantly more important than "cost-effectiveness (B4)". "Resource feasibility (B3)" is also more important than "technical feature fit (B2)" and "cost-effectiveness (B4)", reflecting the extreme importance of frontline support.) The criterion layer weight vector W = (0.44, 0.15, 0.29, 0.12) is obtained through calculation (detailed calculation process using the square root method or eigenvector method is omitted here). A consistency test is then performed, and the calculated CR = 0.04 < 0.1, thus passing the test.

[0146] Steps 3 & 4: Perform single-indicator fuzzy evaluation and comprehensive evaluation The calculation is performed using indicator C1: Re-deployment preparation time (TAT) as an example.

[0147] 1. Define the evaluation set V: V = {Very suitable (v1), Suitable (v2), Average (v3), Not suitable (v4)}.

[0148] 2. Expert Evaluation: Assuming 10 experts are invited to vote on C1 based on criteria B1-B4, the results are as follows: (1) B1 (Task Matching): 8 people voted "Very Suitable", 2 people voted "Suitable". (Modularization greatly improves the frontline rapid repair capability and is highly matched with the task) (2) B2 (Technical Features Fit): 9 people voted "Very Suitable", 1 person voted "Suitable". (Modularization is a common technical trend in modern ground vehicle design) (3) B3 (Feasibility of resource support): 7 people voted "Very suitable", and 3 people voted "Suitable". (Modular replacement has low requirements for support equipment and personnel skills, and is highly feasible) (4) B4 (Cost-effectiveness): 4 people voted "Applicable", 5 people voted "Generally Applicable", and 1 person voted "Not Applicable". (Modular design may increase initial manufacturing costs, but the benefits are reflected in long-term assurance; experts disagree on this.) 3. Construct the fuzzy relation matrix R1: Table 4-2 Fuzzy Relation Matrix

[0149] 4. Perform fuzzy comprehensive evaluation:

[0150] Calculate the components of S1: s1=(0.44×0.8)+(0.15×0.9)+(0.29×0.7)+(0.12×0)=0.352+0.135+0.203+0=0.690 s2=(0.44×0.2)+(0.15×0.1)+(0.29×0.3)+(0.12×0.4)=0.088+0.015+0.087+0.048=0.238 s3=(0.44×0)+(0.15×0)+(0.29×0)+(0.12×0.5)=0+0+0+0.06=0.060 s4=(0.44×0)+(0.15×0)+(0.29×0)+(0.12×0.1)=0+0+0+0.012=0.012 Therefore, S1 = (0.690, 0.238, 0.060, 0.012). This vector has been normalized (0.690 + 0.238 + 0.060 + 0.012 = 1.0).

[0151] Step 5: Calculate the overall score and filter. 1. Assigning scores: Define a score vector P = (90, 70, 50, 30) .

[0152] 2. Calculate the overall score F1 for C1: F1=S1·P =(0.690,0.238,0.060,0.012)·(90,70,50,30)

[0153] =0.690×90+0.238×70+0.060×50+0.012×30 =62.1+16.66+3.0+0.36=82.12 points.

[0154] Repeat steps 3-5 above for all 15 candidate indicators to obtain their combined scores. Assume the calculation results are as follows (partial example): Table 4-3 Comprehensive Scores and Rankings of Indicators (Example)

[0155] 3. Screening Results: Based on the combined scores of all candidate indicators, and with a score threshold of 65, the core guarantee indicator system selected is as follows: {TAT (Time to Ready for Re-deployment), replacement time for critical components, utilization rate of existing similar equipment support facilities, PHM (Progressive Health Management) coverage, spare parts availability, degree of standardization, serialization, and modularization, combat readiness time per unit, autonomous decision-making response level requirements, software fault self-healing capability requirements, mean time between logistic delays (MLDT), and special requirements for wartime support equipment} 4. Results Analysis: The Fuzzy-AHP method, through multi-criteria and fuzzy comprehensive evaluation, derives a hierarchical and logically rigorous system of assurance indicators. This result not only verifies the core conclusions of the QFD method but also provides a more refined quantitative ranking of indicator priorities across four dimensions: task matching, technological fit, resource feasibility, and cost-effectiveness, revealing a deeper level of assurance design guidance.

[0156] V. Comparative Analysis and Summary of Methods The first two chapters proposed two methods for optimizing the guarantee indicator system and provided application examples. This chapter will conduct a systematic comparative analysis of these two methods, summarize their characteristics, advantages and disadvantages, and applicable scenarios, and conclude this study.

[0157] 5.1 Comparative Analysis of Methods To more intuitively compare QFD-based methods and Fuzzy-AHP-based methods, we conduct a comparative analysis from multiple dimensions, as shown in the table below.

[0158] Table 5-1 Comparative Analysis of Two Optimization Methods for Guarantee Indicator Systems

[0159] 5.2 Recommendations for Method Selection Both methods have their advantages and disadvantages, and there is no absolute superiority or inferiority. The choice of which method to use depends on the specific application scenario and objectives.

[0160] 1. Scenarios where QFD is recommended as the preferred method (1) In the early stages of equipment project approval or scheme design, when the main objective is to quickly transform combat mission requirements into support design objectives to guide the design direction.

[0161] (2) When the project team is complex and includes personnel from various departments such as operations, design, and support, an intuitive and visual tool is needed to promote communication and build consensus.

[0162] (3) When time and resources are limited, and a relatively simple, fast and low-cost optimization method is needed.

[0163] 2. Scenarios where the Fuzzy-AHP method is recommended as the preferred choice. (1) In the later stages of equipment design or engineering development, when it is necessary to conduct more in-depth and refined screening and ranking of the initially selected indicators.

[0164] (2) When the evaluation criteria are complex, involving multiple dimensions such as technology, economy, and resources that are difficult to quantify precisely, and when there are disagreements and ambiguities in the opinions of experts.

[0165] (3) When the research or project itself has extremely high requirements for the scientific nature, rigor and traceability of the decision-making process.

[0166] 3. Recommendations for combined applications: In practice, the two methods can also be used in combination to leverage their respective advantages and form a two-step optimization strategy: Step 1 (Coarse Screening): Using the QFD method, a suitable set of candidate indicators is quickly selected from the large general indicator library. This step ensures the "task relevance" of the indicators.

[0167] The second step (refined evaluation): Using the Fuzzy-AHP method, the "candidate indicator set" obtained in the first step is comprehensively evaluated and ranked in a deep and detailed manner, ultimately determining the dedicated support indicator system. This step ensures the "comprehensive suitability" of the indicators.

[0168] This combined strategy balances efficiency and accuracy, making it an ideal approach to solving the optimization problem of complex indicator systems.

Claims

1. A method for optimizing the maintainability and supportability index system of unmanned equipment, characterized in that, Includes the following steps: S1. Mission and Equipment Characteristics Analysis: Obtain mission profile information and equipment structural characteristics information of the target unmanned equipment; S2. Construction of a general supportability index library: Based on general supportability standards, a hierarchical general supportability index library is constructed. This index library includes a target layer for characterizing the supportability level of equipment, a criterion layer including supportability design characteristics and support resources, and specific quantifiable or qualitatively assessable supportability indicators belonging to the criterion layer. S3. Indicator Selection and Optimization: Based on mission profile information, equipment structural characteristics information, and a general supportability indicator library, and in conjunction with the current optimization stage and optimization objectives, select the corresponding single method or combination of methods from a variety of preset indicator optimization methods, perform indicator selection and weight calculation, and form an optimized dedicated supportability indicator system and its weight allocation. S4. Confirmation and Adjustment of Indicator Weights: Confirmation and adjustment of indicator weights in the optimized dedicated support indicator system; S5. Verification and Output of Indicator System: Verify the confirmed and adjusted dedicated guarantee indicator system and form the final output dedicated guarantee indicator system.

2. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 1, characterized in that, In step S3, based on the current optimization stage and optimization objective, a single method or combination of methods is selected from a variety of preset index optimization methods. Specifically, this includes: When the optimization phase is in the early stage of equipment project initiation or scheme design, and the optimization objectives include rapid demand transformation or promoting team consensus, the indicator selection method based on Quality Function Deployment (QFD) and task profile is adopted. When the optimization stage is in the late stage of scheme design or engineering development, and the optimization objective includes multi-dimensional fine evaluation of indicators or handling expert fuzzy judgment, the comprehensive evaluation method of indicators based on fuzzy hierarchical analysis is adopted. When the optimization objective needs to simultaneously consider screening efficiency and evaluation accuracy, the indicator screening method based on Quality Function Deployment (QFD) and task profile is executed in sequence, followed by the comprehensive evaluation method based on Fuzzy Analytic Hierarchy Process (Fuzzy-AHP).

3. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 2, characterized in that, The sequential execution specifically includes: S31. Execute the indicator screening method based on Quality Function Deployment (QFD) and task profile to select a subset of candidate indicators from the general assurance indicator library to ensure the task relevance of the indicators. S32. Using the candidate indicator subset as input, execute the indicator comprehensive evaluation method based on Fuzzy-AHP to perform in-depth and refined comprehensive evaluation and ranking of the candidate indicator subset obtained in the first step, and output an optimized dedicated guarantee indicator system and its weight allocation.

4. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 2, characterized in that, The method for selecting indicators based on Quality Function Deployment (QFD) and task profiles specifically includes: constructing a quality house of task maintainability and support requirements and maintainability and support indicators; quantifying the contribution of each indicator to meeting task requirements through matrix operations; using the contribution as a weight; and selecting general indicator systems based on the weight to form an optimized subset of candidate indicators.

5. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 2, characterized in that, The comprehensive evaluation method for indicators based on the fuzzy hierarchical analysis method specifically includes: constructing a hierarchical structure model for comprehensive evaluation of indicator applicability; determining the weights of evaluation criteria through the hierarchical analysis method; combining the fuzzy comprehensive evaluation to process the fuzzy evaluation of indicator suitability by experts; calculating the comprehensive suitability score of the indicators; and using this as a basis for screening to obtain an optimized dedicated guarantee indicator system and its weight allocation.

6. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 1, characterized in that, Step S4 specifically includes: organizing experts to review the weight allocation of the optimized dedicated support indicator system, and adjusting and correcting the weights based on the review opinions.

7. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 1, characterized in that, Step S4 also includes: simplifying the optimized dedicated security indicator system based on a preset screening threshold, wherein the screening threshold includes a weight threshold or a comprehensive score threshold.

8. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 1, characterized in that, Step S5 specifically includes: verifying the adjusted dedicated support indicator system through at least one of the following methods: expert review, simulation verification, or analogy analysis.

9. The method for optimizing the maintainability and supportability index system of unmanned equipment according to claim 1, characterized in that, The method further includes step S6: dynamically iteratively optimizing the final output dedicated support index system based on changes in the mission profile information, equipment structural characteristic information, or feedback from actual usage method data.

10. A system for optimizing the maintainability and supportability index system of unmanned equipment, characterized in that, The system for implementing the method as described in any one of claims 1-9 comprises: The information acquisition module is used to acquire mission profile information and equipment structural characteristic information of the target unmanned equipment; The indicator library construction module is used to build a general support indicator library that includes equipment supportability design characteristic indicators and support resource indicators based on general supportability standards. The indicator screening and optimization module connects the information acquisition module and the indicator library construction module. It is used to select a single method or combination of methods from a variety of preset indicator optimization methods based on the task profile information, the equipment structural characteristic information and the general support indicator library, combined with the current optimization stage and optimization goal, to screen indicators and calculate weights, and form an optimized special support indicator system and its weight allocation. The indicator weight confirmation and adjustment module, connected to the indicator screening and optimization module, is used to confirm and adjust the indicator weights in the optimized dedicated guarantee indicator system. The indicator system verification and output module is connected to the indicator weight confirmation and adjustment module. It is used to verify the confirmed and adjusted special guarantee indicator system and form the final output special guarantee indicator system. The iterative optimization module, connected to the indicator system verification and output module, is used to dynamically iteratively optimize the final output dedicated support indicator system based on changes in the mission profile information, equipment structural characteristic information, or feedback from actual usage method data.

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