A spacecraft automated testing method and system based on big data

Abstract

The present invention discloses a spacecraft automatic testing method, device, computer equipment and storage medium based on big data, which relates to the field of system testing technology, including classifying business processes and constructing a window tree model; defining system-level behavior states and constructing a behavior state transition diagram model; generating a test case set by traversing the window tree model; actually running the test case to obtain a system-level behavior state transition sequence; verifying the test results; refining the window tree model to optimize aerospace business processes; system behavior states and transition diagram models to deepen system understanding, predict risks, and enhance stability; customizing a test case set based on this model to deeply detect software performance and enhance reliability; prototype testing and state transition analysis use measured data to guide system optimization and ensure stability in complex environments. In-depth analysis strengthens quality control, drives continuous improvement, provides strong support for aerospace missions, and ensures efficient and safe execution.

Description

Technical Field

[0001] The present invention relates to the technical field of system testing, and in particular to a spacecraft automatic testing method and system based on big data. Background Art

[0002] Aerospace engineering has increasingly stringent requirements for the reliability and safety of spacecraft, and big data technology brings new opportunities for automated testing, challenging the limitations of traditional labor-intensive testing. Although models and data-driven methods have been applied to optimization, they still face problems such as rough classification of business processes, vague definitions of system behavior states, and reliance on preset rules for test case generation, resulting in incomplete test coverage and difficulty in accurately predicting faults. Therefore, how to efficiently use big data for spacecraft testing, especially to handle diverse operating scenarios under its complex business processes, becomes the key. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a spacecraft automated testing method, apparatus, computer equipment and storage medium based on big data to solve the problems of low automation level, incomplete coverage and efficiency in spacecraft testing.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a spacecraft automated testing method based on big data, which includes classifying business processes in the spacecraft and building a window tree model based on each category;

[0007] Based on the business process and classification results, define the system-level behavior state and build a behavior state transition diagram model;

[0008] Generate a test case set by traversing the window tree model;

[0009] In the prototype system, the test cases are actually run to obtain the system-level behavior state transition sequence;

[0010] The system-level behavioral state transition sequence is analyzed to verify the test results.

[0011] As a preferred solution of the spacecraft automated testing method based on big data described in the present invention, the business process includes task type, operation steps, dependencies, resource consumption, environment, status monitoring, personnel operation, training records, regulations and standards compliance.

[0012] As a preferred solution of the spacecraft automated testing method based on big data of the present invention, wherein: the business processes in the spacecraft are classified, and the window tree model is constructed based on each category, including:

[0013] Use natural language processing technology to extract the operation steps in the business process;

[0014] Using cluster analysis, we can automatically classify business processes by analyzing the similarity of operation steps and obtain category labels C1, C2, ..., C N , where N represents the number of categories;

[0015] For each category C i , where i = 1, 2, ..., N, and constructs a window tree model W = {W1, W2, ..., W N}, where each window represents a specific type of operation, W i Indicates category C i The window tree structure contains a hierarchy from the root node to the leaf nodes.

[0016] As a preferred solution of the spacecraft automated testing method based on big data described in the present invention, wherein: the system-level behavior state is defined according to the business process and classification results, and the behavior state transition diagram model is constructed, including:

[0017] For each window W i The key operation nodes in define the system-level behavior state S i,j , where i represents the category number, j represents the state number, and all defined states are summarized to form a system-level behavior state set S;

[0018] Each defined system-level behavior state S i,j As a node in the state graph, construct a state node set V;

[0019] According to the historical business process, create a state transition matrix P, where P ij Indicates that from state S i To S j The transition probability is expressed as,

[0020]

[0021] Among them, L ij Indicates that from state S i To S j The number of transfers, L ik Indicates the transition from state to state

[0022] The number of transfers, ∑ k≠i L ik Indicates that from state Si The total number of transitions to all other states, α represents the smoothing factor, and |S| represents the total number of system-level behavioral state sets;

[0023] Based on each P in the state transfer matrix P ij The transition probability is used to construct the transition edge set E;

[0024] According to the state node V and the transition edge set E and the corresponding transition probability P ij , and integrate to obtain the system-level behavioral state transition diagram G=(V,E).

[0025] As a preferred solution of the spacecraft automatic testing method based on big data described in the present invention, wherein: by traversing the window tree model, generating a test case set includes:

[0026] Based on the constructed system-level behavioral state transition graph G, the shortest path algorithm is used to identify each category window W i The critical path in

[0027] Classify the system-level behavior state set S and distinguish the initial state, end state, and fault state;

[0028] Analyze the state transition matrix P and identify the transition probability P ij A state that deviates from the mean;

[0029] Based on the critical path and boundary state, design the test case density function D(S i,j ), expressed as,

[0030]

[0031] Among them, D(S i,j ) indicates state to state S j The priority of the test case selected, represents the boundary importance index, represents the historical risk score, α' represents the system-level behavior state S i,j , β' represents the weight factor of the boundary importance index, and γ' represents the weight factor of the historical risk score;

[0032] For each state S i,j , calculate the priority of the test case according to D(Si,j), and sort all state transitions from high to low priority;

[0033] For each selected state combination S i,j , design specific test scenarios;

[0034] Summarize all the designed test scenarios to form a test case set TC = {TC1, TC2,..., TC M}, where M represents the number of test cases.

[0035] As a preferred solution of the spacecraft automation test method based on big data according to the present invention, wherein: actually running the test cases to obtain the system-level behavior state transition sequence includes,

[0036] Configure the test environment in the prototype system. After importing the test case set TC, write an automated test script. The script should be able to drive the prototype system to execute the predefined operation sequence in the automated test framework, and at the same time monitor and record the system-level behavior state transition during each test execution to form the system-level behavior state transition sequence O sq = {O1, O2,..., O m}, where m represents the total number of steps of the system-level behavior state transition.

[0037] As a preferred solution of the spacecraft automation test method based on big data according to the present invention, wherein: analyzing the system-level behavior state transition sequence to verify the test result includes,

[0038] Compare the system-level behavior state transition sequence O sq with the theoretical transition sequence T sq predicted according to the state transition graph G, and calculate the difference degree between the two using the Hamming distance, expressed as,

[0039]

[0040] where d Hg represents the difference degree;

[0041] Define the difference degree threshold as A;

[0042] When d Hg < A, it indicates that the state transition during the test execution completely coincides with the expectation, and it is defined as verified without error;

[0043] When d Hg ≥ A, it means that there is a difference between the actual state transition and the expectation, and it is defined as verification error. Based on the difference between O sq and T sq , backtrack and analyze the state transition path that does not transfer as expected, and adjust the state transition probability P ij , boundary importance and risk rating until the state transition completely coincides with the expectation, indicating verification without error;

[0044] Calculate the coverage rate F of the test case set TC for the system-level behavior state set S, expressed as,

[0045]

[0046] Among them, |Co(S)| represents the number of states in the system-level behavior state set S actually covered by the test case set TC, and |S| represents the total number of states in the system-level behavior state set S;

[0047] Define the coverage threshold as Q;

[0048] If F is greater than or equal to Q, it means that the overall coverage is high;

[0049] If F is less than Q, it means that the overall coverage is low. In this case, the uncovered states are analyzed and the problems are solved based on the analysis results to improve the overall coverage.

[0050] In a second aspect, the present invention provides a spacecraft automatic testing system based on big data, comprising:

[0051] A window tree model building module is used to classify the business processes in the spacecraft and build a window tree model based on each category;

[0052] A transition diagram building module is used to define system-level behavior states and build a behavior state transition diagram model based on business processes and classification results;

[0053] The test case automatic generation module is used to generate a test case set by traversing the window tree model;

[0054] The test execution module is used to actually run the test cases in the prototype system and obtain the system-level behavior state transition sequence;

[0055] The verification module is used to analyze the system-level behavioral state transition sequence to verify the test results.

[0056] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the spacecraft automated testing method based on big data as described in the first aspect of the present invention is implemented.

[0057] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the spacecraft automated testing method based on big data as described in the first aspect of the present invention.

[0058] The beneficial effects of the present invention are as follows: the refined window tree model effectively organizes aerospace business processes, significantly enhances management efficiency and task logic, and promotes accurate and efficient operations. By constructing a system-level behavioral state and transition graph model, the system mechanism is deeply analyzed, problems are foreseen, stability and security are strengthened, state transitions are optimized, and operational efficiency is improved. On this basis, a customized test case set is generated based on the window tree model to deeply detect software performance and strengthen task reliability. Prototype system testing and state transition analysis verify system consistency, and use measured data to accurately guide system tuning to ensure reliability in complex environments. In-depth state transition analysis strengthens quality control, accurately locates defects, and continuously improves the drive system, laying a solid foundation for the successful implementation of aerospace missions and comprehensively upgrading execution efficiency and safety assurance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0060] Figure 1 This is a flow chart of the spacecraft automated testing method based on big data in Example 1.

[0061] Figure 2 This is a verification diagram of the spacecraft automated test in Example 1. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0063] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0065] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a spacecraft automatic testing method based on big data, comprising the following steps:

[0066] S1. Classify the business processes in the spacecraft and build a window tree model based on each category.

[0067] Collect and organize detailed documentation of spacecraft business processes, including mission types, operational steps, dependencies, resource consumption, environment, condition monitoring, personnel operations, training records, regulations and standards compliance.

[0068] Use text cleaning technology to remove irrelevant information such as stop words and punctuation marks, standardize terminology, and ensure data consistency;

[0069] Use natural language processing technology, especially named entity recognition and semantic parsing, to accurately extract operation steps from text materials;

[0070] Calculate the similarity of the operation steps, use cluster analysis, analyze the similarity of the operation steps, determine the appropriate number of clusters through methods such as the elbow rule and silhouette coefficient, automatically classify the business process, and obtain category labels C1, C2, ..., C N , where N represents the number of categories;

[0071] For each category C i , where i = 1, 2, ..., N, and constructs a window tree model W = {W1, W2, ..., W N}, first determine a root node, which represents the most abstract operation concept or starting state of the category, and then gradually expand the tree structure downward according to the logical order and hierarchical relationship of the operation steps. Each child node represents a more specific operation or state until the leaf node is reached, ensuring that each window tree W i It has a clear hierarchical division and can intuitively display the operation process structure from macro to micro, while maintaining the logical coherence between nodes. Each window represents a specific type of operation. i Indicates category C i The window tree structure contains a hierarchy from the root node to the leaf nodes.

[0072] It should be noted that by carefully classifying the complex spacecraft business processes, we have achieved an orderly organization of the huge mission system. This step not only improves the clarity and efficiency of process management, but also makes the boundaries between various business areas clearer, which is convenient for special management and optimization. The constructed window tree model is used as a visualization tool to provide a hierarchical view from macro to micro for each category. Each level of window represents a set of subtasks under a specific operation category, thereby helping designers and operators to quickly locate, understand and plan tasks.

[0073] S2. Based on the business process and classification results, define the system-level behavioral state and build a behavioral state transition diagram model.

[0074] By analyzing each category of spacecraft business processes W i , identify the critical operation nodes in the classification, and for each window W i The key operation nodes in define the system-level behavior state S i,j , where i represents the category number, j represents the state number, and all defined states are summarized to form a system-level behavior state set S;

[0075] Each defined system-level behavior state S i,j As a node in the state graph, we gather together to construct a state node set V. Each state S i,j Each node exists as an independent node in the state diagram, representing a specific state that the system can be in;

[0076] According to the historical business process, create a state transition matrix P, where P ij Indicates that from state S i To S j The transition probability is expressed as,

[0077]

[0078] Among them, L ij Indicates that from state S i To S j The number of transitions is used to calculate the total number of transitions to all other states, L ik represents the number of transitions from state to state, ∑ k≠i L ik Indicates that from state S i The total number of transfers to all other states is used to accumulate the number of transfers to all non-self states. α represents the smoothing factor, which is a positive number used to handle situations where there is insufficient observation data or a new state to prevent undefined problems caused by zero probability. |S| represents the total number of system-level behavioral state sets, which is used to adjust the scale of the smoothing effect to ensure that a reasonable estimated probability can be given even if no direct observation of the transfer is made between certain states.

[0079] The transition probability formula aims to reasonably estimate the transition probability between states based on historical data by introducing a smoothing factor α. Even when some transition data is scarce, meaningful probability values ​​can be obtained, thereby enhancing the generalization ability of the model.

[0080] Using each element P in the state transfer matrix P ij , if P ij>0, it means there is a j Possible transfers, based on which the transfer edge set E is constructed;

[0081] According to the state node V and the transition edge set E and the corresponding transition probability P ij , and the system-level behavioral state transition diagram G = (V, E) is obtained by integration, which shows the transition relationship and probability of the system between different states, providing a powerful tool for subsequent system analysis, test case generation, performance evaluation and fault prediction.

[0082] It should be noted that in this step, through in-depth analysis of the classified business processes, a series of system-level behavioral states are defined, which directly map the key stages and state transitions of the spacecraft during the mission. The constructed behavioral state transition diagram model not only intuitively shows the dynamic change path of the system from one state to another, but also provides a powerful tool for understanding the system operation logic and predicting potential problems. The beneficial effect of this approach is reflected in enhancing the predictability and controllability of the system, allowing system designers to identify and resolve vulnerabilities in state transitions in advance.

[0083] S3. Generate a test case set by traversing the window tree model.

[0084] Based on the constructed system-level behavioral state transition graph G, the shortest path algorithm is used to identify each category window W i The key path in the system ensures that the test resources are focused on the path that directly affects the execution of the core functions of the system, improving the pertinence and efficiency of the test. The shortest path algorithm helps to quickly locate the key path, ensuring that the test resources are concentrated on the core process of the system and improving the test efficiency.

[0085] Classify the system-level behavior state set S into initial state, end state, and fault state, which helps to identify the starting point, target, and potential failure mode of the test. The initial state refers to the first state before the system starts to perform a specific task, usually corresponding to the ready stage. The end state refers to the final state after completing the task, indicating that a process or operation has been successfully completed. The fault state refers to the abnormal or error state that may occur during the operation of the system, which requires special attention and processing to ensure the safety and stability of the system.

[0086] Analyze the state transition matrix P and identify the transition probability P ij The deviation from the average value means that those uncommon but potentially significant transfers can be captured, further enriching the coverage of the test;

[0087] Based on the critical path and boundary state, combined with the state transition probability, boundary state importance and historical risk score, the test case density function D(S i,j), expressed as,

[0088]

[0089] Among them, D(S i,j ) indicates state to state S j The priority of the test case selected, represents the boundary importance index, which measures the sensitivity and potential risk of the boundary cases or special conditions involved in this transformation. represents the historical risk score, based on the record of errors, failures, or instabilities related to this state in previous tests or operations, and α' represents the system-level behavioral state S i,j The weight factor reflects the state transition probability P ij The importance of the boundary importance index in determining the test priority, β' represents the weight factor of the boundary importance index, emphasizing the importance of the boundary conditions of the state transition in the test, and γ' represents the weight factor of the historical risk score, taking into account the state S i The impact of historical problem records on subsequent tests;

[0090] For each state S i,j , according to D(S i,j ) Calculate the priority of the test case and sort all state transitions from high to low priority;

[0091] For each selected state combination S i,j , design specific test scenarios;

[0092] All designed test scenarios are summarized to form a test case set TC = {TC1, TC2, ..., TC M}, where M represents the number of test cases.

[0093] It should be noted that by utilizing the structural characteristics of the window tree model, a test case set covering various categories and levels of operations is systematically generated through a top-down traversal method. The innovation of this process lies in its comprehensiveness and pertinence, which ensures that the test cases are both extensively covered and deeply detailed. The beneficial effect is that such a test case set can more effectively test the response and performance of the spacecraft software system in various operating scenarios, avoid the omission of important scenarios, improve the effectiveness and completeness of the test, and thus enhance the reliability of the space mission.

[0094] S4. In the prototype system, the test cases are actually run to obtain the system-level behavior state transition sequence.

[0095] Use virtualization technologies (such as Docker containers or VMware) to quickly set up a test environment, ensuring the consistency and reusability of the environment. Utilize mature test frameworks like TestNG or JUnit to manage test cases. These frameworks support various advanced features such as parameterized testing and dependent testing, facilitating the organization and maintenance of complex test suites. Write automated test scripts. Selenium is used for UI automation testing of web applications and mobile applications and can simulate user operations. For the API level, Postman can be used for interface testing. After importing the test case set TC, the script should be able to drive the prototype system to execute a predefined sequence of operations in the automated test framework, while monitoring and recording the system-level behavior state transitions during each test execution. Integrate the ELK Stack for log collection, analysis, and visual monitoring to form the system-level behavior state transition sequence O sq ={O1, O2, ..., O m}, where m represents the total number of steps of the system-level behavior state transitions.

[0096] It should be noted that by executing test cases in the prototype system and directly observing and recording the actual transition sequence of the system-level behavior state, this practical session directly verifies the consistency between the actual performance of the system and the expected design. The beneficial effect of this step is that it provides valuable empirical data, enabling the R & D team to evaluate the stability and functionality implementation degree of the system based on the actual operation situation, promptly discover and correct errors, optimize the system performance, and ensure the reliable operation of the spacecraft in complex environments.

[0097] S5. Analyze the system-level behavior state transition sequence to verify the test results.

[0098] Compare the system-level behavior state transition sequence O sq with the theoretical transition sequence T sq predicted according to the state transition diagram G, and use the Hamming distance to calculate the difference degree between the two, denoted as

[0099]

[0100] where d Hg represents the difference degree;

[0101] Define the difference degree threshold as A;

[0102] When d Hg < A, it indicates that the state transitions during the test execution are completely consistent with the expectations, and it is defined as verified without errors;

[0103] When d Hg ≥ A, it means that there are differences between the actual state transitions and the expectations, and it is defined as verification error. Based on O sq and T sqThe difference in state transition probability P may need to be adjusted. ij , ensuring that the probability distribution is more in line with actual behavior; re-evaluating the importance index of boundary states Ensure that critical boundary status is given adequate attention; at the same time, reconsider historical risk scores To more accurately reflect the risk level of the status. These adjustments are intended to narrow the gap between theory and actual behavior until they are verified;

[0104] Calculate the coverage F of the test case set TC on the system-level behavior state set S, expressed as,

[0105]

[0106] Among them, |Co(S)| represents the number of states in the system-level behavior state set S actually covered by the test case set TC, and |S| represents the total number of states in the system-level behavior state set S;

[0107] Define the coverage threshold as Q;

[0108] If F is greater than or equal to Q, the overall coverage is high, indicating that the test coverage is extensive and the system behavior is well verified;

[0109] If F is less than Q, it means that the overall coverage is low, which means that there are uncovered system states. This requires further analysis of the characteristics and importance of the uncovered states, in-depth analysis of the uncovered states, identification of their classifications, and design of new test cases or optimization of existing test cases based on the analysis results to ensure that these states are effectively tested.

[0110] It should be noted that the accuracy of the test results is verified by conducting an in-depth analysis of the obtained system-level behavior state transition sequence and comparing it with the expected model. This analysis process not only confirms whether the actual working state of the system meets the design requirements, but also reveals the potential laws and abnormal patterns in the state transition process. Its beneficial effect is to strengthen quality control and accurately locate the potential defect bottlenecks of the system in a data-driven way, thereby ensuring the smooth implementation and safe completion of the space mission.

[0111] This embodiment also provides a spacecraft automatic test system based on big data, including:

[0112] A window tree model building module is used to classify the business processes in the spacecraft and build a window tree model based on each category;

[0113] A transition diagram building module is used to define system-level behavior states and build a behavior state transition diagram model based on business processes and classification results;

[0114] The test case automatic generation module is used to generate a test case set by traversing the window tree model;

[0115] The test execution module is used to actually run the test cases in the prototype system and obtain the system-level behavior state transition sequence;

[0116] The verification module is used to analyze the system-level behavioral state transition sequence to verify the test results.

[0117] This embodiment also provides a computer device, which is suitable for the case of a spacecraft automated testing method based on big data, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the spacecraft automated testing method based on big data proposed in the above embodiment.

[0118] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0119] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the spacecraft automated testing method based on big data as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0120] In summary, the present invention effectively organizes aerospace business processes through a refined window tree model, significantly enhances management efficiency and task logic, and promotes accurate and efficient operations. By constructing a system-level behavioral state and transition graph model, the system mechanism is deeply analyzed, problems are foreseen, stability and security are strengthened, state transitions are optimized, and operational efficiency is improved. On this basis, a customized test case set is generated based on the window tree model to deeply detect software performance and strengthen task reliability. Prototype system testing and state transition analysis verify system consistency, and use measured data to accurately guide system tuning to ensure reliability in complex environments. In-depth state transition analysis strengthens quality control, accurately locates defects, and continuously improves the drive system, laying a solid foundation for the successful implementation of aerospace missions and comprehensively upgrading execution efficiency and safety assurance.

[0121] Example 2

[0122] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of a spacecraft automated testing method based on big data is provided.

[0123] In order to verify the effectiveness and innovation of the proposed spacecraft automated testing method based on big data, this embodiment selected the navigation and control subsystem of the spacecraft as the test object, and carried out a detailed test process design and implementation for its business process. First, the task type, operation steps, dependencies, etc. of the navigation and control subsystem were deeply analyzed through natural language processing technology, and a total of 120 operation steps were identified. Then, the K-means clustering algorithm was used to divide them into 5 categories (C1 to C5) according to the similarity of the operations. Each category corresponds to a key task link, such as orbit calculation, attitude control, communication management, etc.

[0124] For each category C i , constructing a detailed window tree model W i , where the window tree W under the C1 category i 1 contains 30 nodes, representing the various operation steps from mission initialization to orbit calculation completion. And so on, to ensure the comprehensive coverage of the entire spacecraft business process. Then, based on these window tree models, the system-level behavior state S is defined. i,j , and constructed a behavioral state transition graph G. In this process, a total of 87 system-level behavioral states were defined, and a state transition graph G containing 236 state transition edges was constructed.

[0125] Based on this state transition diagram, the Dijkstra algorithm is applied to identify the critical path of each category window. At the same time, the state transition characteristics are analyzed according to the state transition matrix P, and 12 states whose transition probabilities deviate significantly from the average are identified. These states are regarded as potential failure points or boundary states. ij The priority of each state transition was calculated, where α'=0.6, β'=0.3, γ'=0.1, ensuring the comprehensiveness and pertinence of the test case design. The designed test case set TC contains 100 test scenarios, ensuring full coverage of critical paths and edge cases.

[0126] In the implementation phase, a precise simulation test environment is configured in the prototype system, the above test case set TC is imported, and the test is executed through the automated test script. sq was recorded in real time, with a total of 1234 state transitions recorded. sq By comparison, using Hamming distance analysis, the calculated difference = 15dHg = 15, which is significantly lower than the preset difference threshold A = 50, verifying that the state transition during the test execution is highly consistent with expectations.

[0127] The details are shown in the following table:

[0128] Table 1 Experimental record table

[0129]

[0130] By comparing the experimental data with the data of the prior art, it can be seen that the embodiments of the present invention significantly improve the test efficiency and accuracy. Specifically, the critical path length of the C1 category is shortened by 18.2% compared with the prior art, reducing the test time; the number of state transitions in the C2 category increases, indicating that the test is more comprehensive; the C3 category clearly identifies 4 fault states, while the prior art lacks this mechanism; the average test case priority of the C4 category is as high as 0.87, indicating that the test case design is more accurate and can effectively focus on key links; the coverage of the C5 category reaches 96.7%, far exceeding the 89.4% of the prior art, which means that the test range is wider and potential problems can be better discovered. In addition, the substantial reduction in the Hamming distance d_Hg proves that the present invention has obvious advantages in ensuring the accuracy of the test results, and verifies the innovation and practicality of the method.

[0131] In summary, this embodiment not only demonstrates the efficiency and accuracy of the spacecraft automation testing method based on big data, but also confirms through specific data its significant advantages in reducing test time, enhancing fault identification capabilities, improving test case targeting, and expanding test coverage. It effectively makes up for the shortcomings of existing technologies and embodies extremely high innovation value and application potential.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A spacecraft automated testing method based on big data, characterized in that: include, Classify the business processes in the spacecraft and build a window tree model based on each category; Based on the business process and classification results, define the system-level behavior state and build a behavior state transition diagram model; Generate a test case set by traversing the window tree model; In the prototype system, the test cases are actually run to obtain the system-level behavior state transition sequence; Analyze the system-level behavioral state transition sequence to verify the test results; The business processes in the spacecraft are classified, and a window tree model is constructed based on each category, including: Use natural language processing technology to extract the operation steps in the business process; Using cluster analysis, we can automatically classify business processes by analyzing the similarity of operation steps and obtain category labels C1, C2, ..., C N , where N represents the number of categories; For each category C i , where i = 1, 2, ..., N, and constructs a window tree model W = {W1, W2, ..., W N }, where each window represents a specific type of operation, W i Indicates category C i The window tree structure includes a hierarchical structure from the root node to the leaf node; Defining the system-level behavior state according to the business process and classification results, and constructing the behavior state transition diagram model includes: For each window W i The key operation nodes in define the system-level behavior state S i，j , where i represents the category number, j represents the state number, and all defined states are summarized to form a system-level behavior state set S; Each defined system-level behavior state S i，j As a node in the state graph, construct a state node set V; According to the historical business process, create a state transition matrix P, where P ij Indicates that from state S i To S j The transition probability is expressed as, Among them, L ij Indicates that from state S i To S j The number of transfers, L ik Indicates that from state S i Transfer to state S k The number of transfers, ∑ k≠i L ik Indicates that from state S i The total number of transitions to all other states, a represents the smoothing factor, and |S| represents the total number of system-level behavioral state sets S; Based on each P in the state transfer matrix P ij The transition probability is used to construct the transition edge set E; According to the state node V and the transition edge set E and the corresponding transition probability P ij , integrate to get the system-level behavior state transition diagram G = (V, E); By traversing the window tree model, the test case set generated includes: Based on the constructed system-level behavioral state transition graph G, the shortest path algorithm is used to identify each category window W i The critical path in Classify the system-level behavior state set S and distinguish the initial state, end state, and fault state; Analyze the state transition matrix P and identify the transition probability P i，j A state that deviates from the mean; Based on the critical path and boundary state, design the test case density function D(S i，j ), expressed as, Among them, D(S i，j ) indicates state S i To state S j The priority of the test case selected, represents the boundary importance index, represents the historical risk score, and α′ represents the system-level behavior state S i，j , β′ represents the weight factor of the boundary importance index, and γ′ represents the weight factor of the historical risk score; For each state S i，j , according to D(S i，j ) Calculate the priority of the test case and sort all state transitions from high to low according to the priority D; For each selected state combination S i，j , design specific test scenarios; All designed test scenarios are summarized to form a test case set TC = {TC1, TC2, ..., TC M }, where M represents the number of test cases; The actual running of the test case to obtain the system-level behavior state transition sequence includes: After configuring the test environment in the prototype system and importing the test case set TC, write the automated test script. The script should be able to drive the prototype system to execute the predefined operation sequence in the automated test framework, and monitor and record the system-level behavior state transition during each test execution to form a system-level behavior state transition sequence. sq ={O1, O2, ..., O m }, where m represents the total number of system-level behavioral state transitions; The analysis of the system-level behavior state transition sequence to verify the test results includes: The system-level behavior state transition sequence O sq Compared with the theoretical transition sequence T predicted by the state transition diagram G sq , use the Hamming distance to calculate the difference between the two, expressed as, Among them, d Hg Indicates the degree of difference; Define the difference threshold as A; When Hg When <A, it indicates that the state transition during the test execution is completely consistent with the expectation, and the definition verification is correct; When Hg When ≥A, it means that the actual state transfer is different from the expected one, and the verification error is defined. sq With T sq The difference between the state transition paths that did not transfer as expected is analyzed retrospectively, and the state transition probability P is adjusted. ij , Boundary Importance and risk rating Until the state transfer is completely consistent with the expectation, it means that the verification is correct; Calculate the coverage F of the test case set TC on the system-level behavior state set S, expressed as, Where |Co(S)| represents the number of states in the system-level behavior state set S actually covered by the test case set TC, and |S| represents the total number of states in the system-level behavior state set S; Define the coverage threshold as Q; If F is greater than or equal to Q, it means that the overall coverage is high; If F is less than Q, it means that the overall coverage is low. In this case, the uncovered states are analyzed and the problems are solved based on the analysis results to improve the overall coverage.

2. The spacecraft automated testing method based on big data as claimed in claim 1, characterized in that: The business process includes task type, operation steps, dependencies, resource consumption, environment, status monitoring, personnel operation, training records, regulations and standards compliance.

3. A spacecraft automated testing system based on big data, based on the spacecraft automated testing method based on big data according to any one of claims 1 to 2, characterized in that: include, A window tree model building module is used to classify the business processes in the spacecraft and build a window tree model based on each category; A transition diagram building module is used to define system-level behavior states and build a behavior state transition diagram model based on business processes and classification results; The test case automatic generation module is used to generate a test case set by traversing the window tree model; The test execution module is used to actually run the test cases in the prototype system and obtain the system-level behavior state transition sequence; The verification module is used to analyze the system-level behavioral state transition sequence to verify the test results.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the spacecraft automatic testing method based on big data described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the spacecraft automatic testing method based on big data described in any one of claims 1 to 2 are implemented.

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