Carrier rocket generalization test experiment optimization method based on matrix analysis
By optimizing the launch vehicle testing process through matrix analysis and a comprehensive cost-benefit model, the problems of repetitive testing and high costs are solved, achieving efficient and low-cost testing process optimization, which is applicable to various types of launch vehicles and large-scale industrial testing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING INST OF ASTRONAUTICAL SYST ENG
- Filing Date
- 2025-12-09
- Publication Date
- 2026-05-05
AI Technical Summary
In existing technologies, the launch vehicle testing process suffers from problems such as repetitive testing, large workload, long testing cycle, and high cost, making it difficult to adapt to the development trend of ultra-high-density launches.
A matrix analysis-based optimization method for generalized test and experimentation of launch vehicles is adopted. By constructing a multi-dimensional correlation matrix and a comprehensive cost-benefit model, the test process is optimized, redundant tests are reduced, non-critical items are eliminated, and test efficiency is improved.
This method optimizes the testing process without compromising quality, improves testing efficiency, reduces costs, and provides multi-objective optimal decision-making solutions, applicable to the optimization of testing processes for various types of launch vehicles.
Smart Images
Figure CN121979774A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a generalized test optimization method for launch vehicles based on matrix analysis, belonging to the field of launch vehicle test and launch process optimization technology. Background Technology
[0002] Launch vehicles are the prerequisite and foundation for all human space activities. Characterized by high technology, high risk, and high cost, they are the strategic cornerstone of a nation's space science, space technology, space applications, and security systems. In recent years, major spacefaring nations worldwide have accelerated their space economy development, leading to an increasing demand for access to space. As the transportation vehicle that sends payloads from Earth into space orbit, launch vehicles are a direct reflection of a nation's space access capabilities and an important indicator of its modern scientific and technological development level and comprehensive national strength.
[0003] Complex testing procedures are one of the key factors currently hindering the optimization of my country's launch vehicle testing process. In the early stages of model development, numerous test items are typically set up to ensure comprehensive testing coverage, which plays a positive role in ensuring successful launches. However, with increasingly mature technology, traditional testing procedures have also brought problems such as repetitive testing, large workload, long testing cycles, and high costs, making them no longer suitable for the current trend of ultra-high-density launches. Summary of the Invention
[0004] The technical problem solved by this invention is that, in the current technology, the reverse optimization design approach is difficult to adapt to the problem of repeated testing, large testing workload, long testing cycle and high cost caused by the trend of ultra-high density launches. Therefore, a generalized test optimization method for launch vehicles based on matrix analysis is proposed.
[0005] The present invention solves the above-mentioned technical problem through the following technical solution: A matrix analysis-based optimization method for generalized test experiments of launch vehicles includes: The core elements of the generalized test of launch vehicles were analyzed and identified, and a multi-dimensional correlation matrix among the core elements was constructed. Construct a comprehensive cost-benefit model for the testing and experimentation process; Based on the requirements of economic efficiency, quality, constraints, and dominance in testing and experimentation, the comprehensive cost-benefit model of the testing and experimentation process is reorganized and optimized. Based on the comprehensive cost-benefit model of the reorganized and optimized testing process, the steps of the testing process are optimized to complete both algorithm optimization and process optimization.
[0006] The core elements include aircraft test items, aircraft equipment interfaces, and aircraft equipment functions. The multi-dimensional correlation matrix between these core elements is the test-function-interface relationship matrix, which is used to clarify the correspondence between aircraft test items, aircraft equipment interfaces, and aircraft equipment functions.
[0007] The aircraft test items include general electrical system test items, energy management test items, data communication test items, GNC function test items, on-board integrated test items, and system-level test items. Each type of test item includes specific test items. After clarifying the correspondence between the functions of the equipment on the aircraft and the interfaces of the equipment on the aircraft, the correspondence is matched with the specific test items to obtain the test-function-interface relationship matrix.
[0008] The method for constructing a comprehensive cost-benefit model for the testing and experimentation process is as follows: Define the optimization objectives and list the experimental time cost and corresponding main factors for each specific test item; By setting parameter items, we can characterize the situations in the test process where each specific test item cannot exist simultaneously; The test status of each specific test item is characterized by setting parameter items; Based on the set parameters, construct the comprehensive cost-benefit model expression.
[0009] The optimization goal is to reduce the number of test executions and the cumulative time cost of test items at the experimental level, thereby achieving optimization of test items and states. The matrix represents the specific test items and test status carried out in each test experiment; α and β are the weight coefficients of the optimization objective.
[0010] When reorganizing and optimizing the comprehensive cost-benefit model of the testing and experimentation process at the algorithm level, the following economic principles of testing and experimentation should be followed: For experimental objectives with high economic indicators, optimization is performed at the single-experiment level. The time consumption of each experiment is sequentially screened, and the experiment level is merged. It is also considered whether there is repeated testing of individual test items between different experiments. Based on the repeated testing situation, the experiment is merged and optimized to achieve the economic optimization goal.
[0011] The quality principle is as follows: The key objectives of each optimization objective are ranked, and the test items with the highest key ranking are prioritized. The second-best or any test items are moved to other test items. At the same time, the overall test coverage of the step test meets the task requirements.
[0012] The aforementioned restrictive and dominant requirements are: If two tests have the same type of test conditions or test items, and there is a dominant relationship between the two, then the two tests are merged, and the test item with the higher degree of dominance is retained. If quality principles are involved at the same time, they are considered together. During the algorithm optimization process, each newly generated test experiment must verify whether the constraints and dominance requirements are met.
[0013] The process optimization method is as follows: Before the official launch, the test and experimental process is optimized and improved based on the specific requirements of the process route, spacecraft status, interaction with other subsystems of the launch system, test items and test nodes, launch procedures and safety assurance involved at the launch site.
[0014] The advantages of this invention compared to the prior art are: (1) The present invention provides a general test optimization method for launch vehicles based on matrix analysis. The general test optimization method for launch vehicles based on matrix analysis can optimize the test process and improve test efficiency without reducing quality by combining the test coverage analysis results. By integrating test specifications and core elements, the method can reduce the number of test items and states at the test level by optimizing the total number of executions of test items and the accumulation of test items / states. Finally, by optimizing the combination of test items and tests, a multi-objective optimal decision scheme is formed in terms of both algorithm optimization and test process optimization. (2) This invention integrates professional knowledge of the testing process, uses matrix analysis and artificial intelligence methods to perform integrated analysis and optimization, and provides the optimal decision-making scheme. It is also general and can be applied to the optimization of the testing process of various types of launch vehicles in my country, as well as to the reconstruction and optimization design of various large-scale industrial testing processes. Attached Figure Description
[0015] Figure 1 The flowchart of the generalized test optimization method provided by the present invention; Figure 2 This is a schematic diagram of the relationship matrix between the various functions of the test experiment and the system provided by the present invention. Detailed Implementation
[0016] A generalized test optimization method for launch vehicles based on matrix analysis is obtained through continuous reduction and optimization. Starting from the forward design perspective, it adopts matrix analysis technology, a test modeling method based on matrix analysis, and a test recombination optimization method based on heuristic algorithms. It focuses on combinatorial optimization technology represented by matrix analysis, takes the test state as input, and forms the relevant test process by optimally combining test items, thereby completing algorithm optimization and test process optimization.
[0017] A matrix analysis-based optimization method for generalized test experiments of launch vehicles, comprising the following steps: The core elements of the generalized test of launch vehicles were analyzed and identified, and a multi-dimensional correlation matrix among the core elements was constructed. Construct a comprehensive cost-benefit model for the testing and experimentation process; Based on the requirements of economic efficiency, quality, constraints, and dominance in testing and experimentation, the comprehensive cost-benefit model of the testing and experimentation process is reorganized and optimized. Based on the comprehensive cost-benefit model of the reorganized and optimized testing process, the steps of the testing process are optimized to complete both algorithm optimization and process optimization.
[0018] The core elements include aircraft test items, aircraft equipment interfaces, and aircraft equipment functions. The multi-dimensional correlation matrix between these core elements is the test-function-interface relationship matrix, which is used to clarify the correspondence between aircraft test items, aircraft equipment interfaces, and aircraft equipment functions.
[0019] The aircraft test items include general electrical system test items, energy management test items, data communication test items, GNC function test items, on-board integrated test items, and system-level test items. Each type of test item includes specific test items. After clarifying the correspondence between the functions of the equipment on the aircraft and the interfaces of the equipment on the aircraft, the correspondence is matched with the specific test items to obtain the test-function-interface relationship matrix.
[0020] The method for constructing a comprehensive cost-benefit model for the testing and experimentation process is as follows: Define the optimization objectives and list the experimental time cost and corresponding main factors for each specific test item; By setting parameter items, we can characterize the situations in the test process where each specific test item cannot exist simultaneously; The test status of each specific test item is characterized by setting parameter items; Based on the set parameters, construct the comprehensive cost-benefit model expression.
[0021] The optimization objective expression is:
[0022] In the formula, the parameter appearing for the first time needs to be explained; The optimization goal is to reduce the number of test executions and the cumulative time cost of test items at the experimental level, thereby achieving optimization of test items and states. The matrix represents the specific test items and test status carried out in each test experiment; α and β are the weight coefficients of the optimization objective.
[0023] The expressions for the test time cost and corresponding main factors for each specific test item are as follows:
[0024] in, This indicates the time cost of each experiment; ,
[0025] In the formula, P represents the specific items and test states carried out in each test, and T represents the time consumed by each test item or test state. This represents the correspondence between the k-th test and the i-th test item or test state to be assessed. If the i-th test item or test state is assessed in the current test, then set it to... Otherwise take ; This indicates the test items or test status that need to be assessed; Indicates the number of tests. This represents the time cost metric, which is the cost required to test the i-th test item or test state in the k-th test.
[0026] The expression for the condition in the P matrix where some test items or experimental states cannot exist simultaneously is:
[0027] The expression for the test state of each specific test item is as follows:
[0028] In the formula, P(K) represents the final state of the matrix, that is, the test state in the last test (the Kth test) needs to be consistent with the launch state. Maintain consistency.
[0029] The expression for whether the test status of each specific test item meets the coverage requirement is as follows:
[0030] In the formula, XC(k) represents the test coverage of each test item / state in the P matrix, and the specific calculation method is as follows: Indicator function:
[0031] When reorganizing and optimizing the comprehensive cost-benefit model of the testing and experimentation process at the algorithm level, the following economic principles of testing and experimentation should be followed: For experimental objectives with high economic indicators, optimization is performed at the single-experiment level. The time consumption of each experiment is sequentially screened, and the experiment level is merged. It is also considered whether there is repeated testing of individual test items between different experiments. Based on the repeated testing situation, the experiment is merged and optimized to achieve the economic optimization goal.
[0032] The principle of quality is: The key objectives of each optimization objective are ranked, and the test items with the highest key ranking are prioritized. The second-best or any test items are moved to other test items. At the same time, the overall test coverage of the step test meets the task requirements.
[0033] The restrictive and dominant requirements are: If two tests have the same type of test conditions or test items, and there is a dominant relationship between the two, then the two tests are merged, and the test item with the higher degree of dominance is retained. If quality principles are involved at the same time, they are considered together. During the algorithm optimization process, each newly generated test experiment must verify whether the constraints and dominance requirements are met.
[0034] The process optimization method is as follows: Before the official launch, the test and experimental process is optimized and improved based on the specific requirements of the process route, spacecraft status, interaction with other subsystems of the launch system, test items and test nodes, launch procedures and safety assurance involved at the launch site.
[0035] The following description, in conjunction with the accompanying drawings and preferred embodiments, provides further details: In the current embodiment, the algorithm flowchart is as follows: Figure 1 As shown: The specific steps are as follows: (1) Combining expert systems and test knowledge, the experiment-function-interface relationship matrix analysis method is adopted to construct the correlation matrix between the core elements of the test (i.e., "experiment-function-interface"); (2) Based on matrix analysis, test experiment modeling is carried out, focusing on problem definition, and a comprehensive cost-benefit model of the test process is constructed with human resource cost, test coverage and other factors as the core. (3) A heuristic solution algorithm is adopted, which relies on economy and quality to reorganize and optimize the test experiment, and follows the constraints and dominance in the solution process.
[0036] The specific steps to be noted include: Optimization of electrical system testing procedures: Electrical system testing is a crucial part of launch vehicle testing and launch operations, characterized by numerous test components, complex testing coverage requirements, and tightly coupled interrelationships between these components. With the increasing demands of the aerospace market and the rapid development of aerospace missions, the importance of optimizing the launch vehicle testing and launch process is growing. The launch site process refers to a comprehensive set of technical solutions implemented before launch, encompassing the technological routes, the status of key technologies, their interrelationships with other subsystems, major work items and timelines, launch procedures, and safety assurance measures. It is the prerequisite and foundation for ensuring the successful completion of a space launch mission.
[0037] Optimization of generalized testing and experimentation: The core of the test optimization method is to optimize the test experiments composed of test items, primarily using matrix analysis techniques. Methods include test-function-interface relationship matrix analysis, matrix analysis-based test modeling, and heuristic algorithm-based test reorganization optimization. Test optimization mainly focuses on combinatorial optimization techniques, represented by matrix analysis. Using the results of test item optimization as input, it forms relevant test experiments by optimally combining test items, requiring the sequential application of the three methods mentioned above to achieve optimization.
[0038] The method for analyzing the experiment-function-interface relationship matrix is as follows: The test-function-interface relationship matrix analysis method primarily addresses the functional and interface coverage issues that test experiments need to resolve. Each test project can assess one or more functions, performance, and interfaces. This method clarifies the relationships between test projects and functional performance and interfaces, breaking down barriers between core test elements (i.e., "test-function-interface").
[0039] By combining different test and experimental status checklists and test statuses, a relationship matrix between tests and various equipment interfaces is constructed. This involves reviewing system-level test items and their corresponding functional assessments within the electrical system, and further clarifying the correspondence between interfaces and functions within the electrical system. Figure 2 As shown; Taking the testing specifications as an example, it can be seen that the system-level test items in an electrical system mainly include system-level general test items, control system test items, measurement and control and communication system test items, fault detection system test items, and ground measurement and control system test items. The assessment functions involve general testing, energy management, data communication, GNC function, and on-board integration, etc. The corresponding relationship is shown in the following example.
[0040]
[0041] The steps for test experiment modeling based on matrix analysis are as follows: The test experiment modeling method based on matrix analysis mainly focuses on problem definition, constructing a comprehensive cost-benefit model of the test process with human resource costs and test coverage as the core, and sorting out the constraints such as resource consumption in the test process.
[0042] (1) (2) , (3) (4) (5) (6) (7) Equation (1) is the optimization objective. It mainly achieves the reduction of test items and states at the test level by optimizing the total number of executions of test items and the sum of test items / states. The matrix represents which projects are carried out / what states are selected in each test experiment.
[0043] Equation (2) mainly represents the time cost of each experiment, where This indicates the time required for each test or experiment.
[0044] Equation (3) represents the main factors affecting the time required for each test, where P indicates which items / states are carried out in each test, and T represents the time consumed by each item / state. This represents the correspondence between the k-th test and the i-th test item / state that needs to be assessed. If the i-th test item / state is assessed in this test, then it is set to... Conversely, take ; This indicates the test items / test status that need to be assessed; This indicates the number of tests / trials. This represents the time cost metric, specifically the cost required to test the i-th test item / test state in the k-th test.
[0045] Equation (4) mainly indicates that some items / states in the P matrix cannot exist at the same time (set to 1), such as using simulated flight and emergency shutdown.
[0046] Equation (5) mainly indicates that the final state of matrix P needs to be the launch state. In the last test (the Kth test), the test item / test state matrix is as follows: It needs to be consistent with the launch status. Maintain consistency.
[0047] Equation (6) mainly indicates that all items / states in each test of the P matrix need to be fully covered after K tests.
[0048] Equation (7) mainly represents the calculation method of test coverage of each test item / state test in the P matrix.
[0049] The following is an example of a P matrix:
[0050] The heuristic-based test experiment reorganization optimization algorithm is as follows: Heuristic solution algorithms primarily rely on economy and quality in their solutions, and adhere to constraints and dominance principles during the solution process.
[0051] Regarding economic efficiency, priority is given to optimizing experiments with high economic indicators. Optimization begins at the individual experiment level, with the time consumed by each experiment being sequentially filtered. Experimental merging is attempted, and then the possibility of duplicate testing of individual items across different experiments is considered to determine if there is room for optimization. Regarding quality, for experiments involving the possibility of experiment merging, more critical items are prioritized for evaluation, while less critical items can be moved to other experiments for evaluation. Simultaneously, it is necessary to ensure that the overall test coverage meets the requirements.
[0052] During the solution process, regarding dominance and constraints, if two tests have the same type of test states or test items, and there is a dominance relationship between them, then the two tests can be merged, and the test item with the higher degree of dominance should be retained. If quality is also involved, they need to be considered together. Furthermore, in the algorithm, the constraints of each newly generated test must be verified to ensure they are satisfied.
[0053] In this embodiment, addressing the challenge of complex testing procedures hindering the optimization of my country's launch vehicle testing process, this invention proposes a matrix analysis-based optimization method for generalized launch vehicle testing. This method, combined with test coverage analysis results, optimizes the testing process and improves testing efficiency without compromising quality. By employing the matrix analysis-based optimization method for generalized launch vehicle testing proposed in this invention, using test conditions as input, and optimizing the combination of test items to generate relevant test optimization results, a comprehensive cost-benefit optimal decision-making scheme can be quickly provided.
[0054] By employing the matrix analysis-based generalized test optimization method for launch vehicles proposed in this invention, and taking the test status as input, the optimal combination of test items is used to form relevant test optimization results, and a comprehensive cost-benefit optimal decision scheme is given.
[0055] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.
[0056] The contents not described in detail in this specification are common knowledge to those skilled in the art.
Claims
1. A method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis, characterized in that... include: The core elements of the generalized test of launch vehicles were analyzed and identified, and a multi-dimensional correlation matrix among the core elements was constructed. Construct a comprehensive cost-benefit model for the testing and experimentation process; Based on the requirements of economic efficiency, quality, constraints, and dominance in testing and experimentation, the comprehensive cost-benefit model of the testing and experimentation process is reorganized and optimized. Based on the comprehensive cost-benefit model of the reorganized and optimized testing process, the steps of the testing process are optimized to complete both algorithm optimization and process optimization.
2. The method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis according to claim 1, characterized in that: The core elements include aircraft test items, aircraft equipment interfaces, and aircraft equipment functions. The multi-dimensional correlation matrix between these core elements is the test-function-interface relationship matrix, which is used to clarify the correspondence between aircraft test items, aircraft equipment interfaces, and aircraft equipment functions.
3. The method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis according to claim 2, characterized in that: The aircraft test items include general electrical system test items, energy management test items, data communication test items, GNC function test items, on-board integrated test items, and system-level test items. Each type of test item includes specific test items. After clarifying the correspondence between the functions of the equipment on the aircraft and the interfaces of the equipment on the aircraft, the correspondence is matched with the specific test items to obtain the test-function-interface relationship matrix.
4. The method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis according to claim 2, characterized in that: The method for constructing a comprehensive cost-benefit model for the testing and experimentation process is as follows: Define the optimization objectives and list the experimental time cost and corresponding main factors for each specific test item; By setting parameter items, we can characterize the situations in the test process where each specific test item cannot exist simultaneously; The test status of each specific test item is characterized by setting parameter items; Based on the set parameters, construct the comprehensive cost-benefit model expression.
5. The method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis according to claim 4, characterized in that: The optimization objective expression is: The optimization goal is to reduce the number of test executions and the cumulative time cost of test items at the experimental level, thereby achieving optimization of test items and states. The matrix represents the specific test items and test status carried out in each test experiment; α and β are the weight coefficients of the optimization objective. The expressions for the test time cost and corresponding main factors for each specific test item are as follows: in, This indicates the time cost of each experiment; , In the formula, P represents the specific items and test states carried out in each test, and T represents the time consumed by each test item or test state. This represents the correspondence between the k-th test and the i-th test item or test state to be assessed. If the i-th test item or test state is assessed in the current test, then set it to... Otherwise take ; This indicates the test items or test status that need to be assessed; Indicates the number of tests. This represents the time cost metric, which is the cost required to test the i-th test item or test state in the k-th test.
6. The method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis according to claim 5, characterized in that: The expression for the condition in the P matrix where some test items or experimental states cannot coexist is as follows: The expression for the test state of each specific test item is as follows: In the formula, P(K) represents the final state of the matrix. In the last test, i.e., the Kth test, the state needs to be consistent with the launch state. Maintain consistency; The expression for whether the test status of each specific test item meets the coverage requirement is as follows: In the formula, XC(k) represents the test coverage of each test item / state in the P matrix, and the specific calculation method is as follows: This is an indicator function.
7. The method for optimizing the generalized test and experiment of launch vehicles based on matrix analysis according to claim 5, characterized in that: When reorganizing and optimizing the comprehensive cost-benefit model of the testing and experimentation process at the algorithm level, the following economic principles of testing and experimentation should be followed: For experimental objectives with high economic indicators, optimization is performed at the single-experiment level. The time consumption of each experiment is sequentially screened, and the experiment level is merged. It is also considered whether there is repeated testing of individual test items between different experiments. Based on the repeated testing situation, the experiment is merged and optimized to achieve the economic optimization goal.
8. The method for optimizing the generalized test of launch vehicles based on matrix analysis according to claim 7, characterized in that: The quality principle is as follows: The key objectives of each optimization objective are ranked, and the test items with the highest key ranking are prioritized. The second-best or any test items are moved to other test items. At the same time, the overall test coverage of the step test meets the task requirements.
9. The method for optimizing a generalized test and experiment of a launch vehicle based on matrix analysis according to claim 7, characterized in that: The aforementioned restrictive and dominant requirements are: If two tests have the same type of test conditions or test items, and there is a dominant relationship between the two, then the two tests are merged, and the test item with the higher degree of dominance is retained. If quality principles are involved at the same time, they are considered together. During the algorithm optimization process, each newly generated test experiment must verify whether the constraints and dominance requirements are met.
10. The method for optimizing a generalized test and experiment of a launch vehicle based on matrix analysis according to claim 9, characterized in that: The process optimization method is as follows: Before the official launch, the test and experimental process is optimized and improved based on the specific requirements of the process route, spacecraft status, interaction with other subsystems of the launch system, test items and test nodes, launch procedures and safety assurance involved at the launch site.