A test case generation method and generation platform for aircraft performance testing

Abstract

This application belongs to the field of software testing technology and discloses a test case generation method and generation platform for aircraft performance testing. The method determines a first input parameter set and generates a first test case using a global random search; uses the first test case to drive a flight management module to perform a simulation test and receives first flight status data output by the flight management module after the simulation test; determines a second input parameter set based on the first flight status information and generates a second test case using a local random search; uses the second test case to drive the flight management module to perform a simulation test and receives second flight status data output by the flight management module after the simulation test; and determines the out-of-control input parameter based on the second flight status data. By optimizing the generation and execution of test cases through intelligent algorithms, the method improves the efficiency of test case generation, reduces testing costs, ensures more comprehensive test coverage, and improves test reliability.

Description

Technical Field

[0001] The present application relates to the field of software testing technology, and in particular to a test case generation method and generation platform for aircraft performance testing. Background Art

[0002] The Vehicle Management System (VMS) is a comprehensive system that covers key subsystems such as the Flight Management System (FMS) and the Propulsion Utility Management System (UMS) to ensure the safe flight of the aircraft. The core advantage of VMS lies in its comprehensive management capabilities. Through integrated design concepts, multiple functions and subsystems are coordinated as a whole. As the core control system of aircraft such as drones, the performance and stability of the flight management system are directly related to the flight performance and safety of the aircraft. In order to ensure the high performance and stability of this critical system, it must be rigorously tested and evaluated. However, traditional testing methods mainly rely on manual operations, including writing test cases and manually executing tests. This method is not only inefficient, but also has limited test coverage, and it is difficult to meet the high testing requirements of modern aircraft. Summary of the Invention

[0003] To this end, an embodiment of the present application provides a test case generation method and generation platform for aircraft performance testing, which can improve the efficiency of test case generation while effectively improving test coverage.

[0004] In a first aspect, the present application provides a test case generation method for aircraft performance testing.

[0005] This application is achieved through the following technical solutions:

[0006] A test case generation method for aircraft performance testing, comprising:

[0007] Determine a first input parameter set, and generate a first test case using a global random search based on the first input parameter set;

[0008] Sending the first test case to a flight management module, driving the flight management module to perform a simulation test, and receiving first flight status data output by the flight management module after the simulation test;

[0009] determining a second input parameter set based on the first flight state information, and generating a second test case using a local random search based on the second input parameter set;

[0010] Sending the second test case to a flight management module, driving the flight management module to perform a simulation test, and receiving second flight status data output by the flight management module after the simulation test;

[0011] A loss of control input parameter is determined based on the second flight state data.

[0012] In a preferred example of the present application, it may be further configured that the determining of the first input parameter set and the generating of the first test case by using a global random search based on the first input parameter set include:

[0013] Determining multiple input parameters of the aircraft under test in various flight mission phases and corresponding value ranges of each input parameter, wherein the flight mission phases include a takeoff phase, a flight phase, and a landing phase;

[0014] Randomly select two random values ​​within the value range of each input parameter, where the two random values ​​are located in the first half and the second half of the value range respectively;

[0015] All input parameters, corresponding random values ​​and different flight mission phases are combined to generate the first test case.

[0016] In a preferred example of the present application, it can be further configured that the flight management module includes a simulation unit, a flight management calculation unit, and a flight simulation calculation unit, and the first test case is sent to the flight management module, and the flight management module is driven to perform a simulation test, including:

[0017] Using the first test case to drive multiple simulation systems in the simulation unit to perform simulation testing to obtain a first simulation result;

[0018] The flight management calculation unit adjusts the flight characteristic parameters based on the first simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit;

[0019] The flight simulation calculation unit performs comprehensive calculation on the adjusted flight characteristic parameters and the first simulation result to obtain first flight status data.

[0020] In a preferred example of the present application, it can be further configured that: a second input parameter set is determined based on the first flight status information; and a second test case is generated based on the second input parameter set using a local random search, including:

[0021] Comparing the first flight status data with reference flight status data, and determining potential fault input parameters, potential fault flight stages, and potential fault value ranges for abnormal flight of the aircraft under test based on the comparison results;

[0022] The potential failure flight phases are subdivided into the take-off phase and the climb phase, the flight phase is subdivided into the leveling phase, the cruising phase and the circling phase, and the landing phase is subdivided into the approach phase and the landing phase;

[0023] The potential fault value interval is divided into four subintervals, and a random input value is randomly determined in each subinterval;

[0024] The potential fault input parameters, potential fault flight phase and random input values ​​are combined to generate a second test case.

[0025] In a preferred example of this application, it can be further set as follows:

[0026] The flight management module includes a simulation unit, a flight management calculation unit, and a flight simulation calculation unit. The second test case is sent to the flight management module, and the flight management module is driven to perform a simulation test, including:

[0027] Using the second test case to drive multiple simulation systems in the simulation unit to perform simulation testing to obtain a second simulation result;

[0028] The flight management calculation unit adjusts the flight characteristic parameters based on the second simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit;

[0029] The flight simulation calculation unit performs comprehensive calculation on the adjusted flight characteristic parameters and the second simulation result to obtain second flight status data.

[0030] In a preferred example of the present application, it can be further configured that, after generating the first test case, the method further includes: performing sensitivity analysis on the first test case;

[0031] Using the gradient method to analyze the influence of each input parameter on the output result in the first test case;

[0032] Determine a logical relationship between the input parameters based on the influence degree, wherein the logical relationship includes AND, OR, NOT and SUBJECT;

[0033] Based on the logical relationship, redundant test cases in the first test cases are identified and the redundant test cases are deleted.

[0034] In a preferred example of the present application, it can be further configured that after generating the second test case, the following is further included:

[0035] Used to perform dimensionality reduction on the second test case using the trained random forest model.

[0036] In a second aspect, the present application provides a test case generation platform for aircraft performance testing.

[0037] This application is achieved through the following technical solutions:

[0038] A test case generation platform for aircraft performance testing, executing the test case generation platform method described in the first aspect, comprising:

[0039] Test environment module, data exchange module and flight management module;

[0040] The test environment module includes a first generation unit and a second generation unit, wherein the first generation unit is used to determine a first input parameter set, generate a first test case based on the first input parameter set using a global random search, send the first test case to a flight management module for simulation testing, and receive first flight status data of the flight management module; the second generation unit is used to determine a second input parameter set based on the first flight status data, generate a second test case based on the second input parameter set using a local random search, send the second test case to the flight management module for simulation testing, receive second flight status data of the flight management module, and determine an out-of-control input parameter based on the second flight status data;

[0041] a data exchange module, configured to cache interaction data between the test environment module and the flight management module, the interaction data including a first test case, first flight status data, a second test case, and second flight status data;

[0042] The flight management module is used to receive the first test case, perform simulation testing based on the first test case, and obtain first flight status data; receive the second test case, perform simulation testing based on the second test case, and obtain second flight status data.

[0043] In a preferred example of this application, it can be further set as follows:

[0044] The test environment module further includes a first sensitivity analysis unit and a second sensitivity analysis unit;

[0045] The first sensitivity analysis unit is configured to analyze the degree of influence of each input parameter in the first test case on the output result using a gradient method, determine a logical relationship between the input parameters based on the degree of influence, wherein the logical relationship includes AND, OR, NOT, and dependency, identify redundant test cases in the first test case based on the logical relationship, and delete the redundant test cases;

[0046] The second sensitivity analysis unit is used to perform dimensionality reduction processing on the second test case using the trained random forest model.

[0047] Thirdly, this application is achieved through the following technical solutions:

[0048] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of any one of the above-mentioned test case generation methods for aircraft performance testing.

[0049] In summary, compared with the prior art, the technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0050] The test case generation method of the present application determines a first input parameter set, generates a first test case based on the first input parameter set using a global random search; sends the first test case to a flight management module, drives the flight management module to perform a simulation test, and receives first flight status data output by the flight management module after the simulation test; determines a second input parameter set based on the first flight status information, generates a second test case based on the second input parameter set using a local random search; sends the second test case to the flight management module, drives the flight management module to perform a simulation test, and receives second flight status data output by the flight management module after the simulation test; determines the out-of-control input parameter based on the second flight status data. The generation and execution of test cases are optimized through intelligent algorithms, thereby improving the efficiency of test case generation, reducing testing costs, and ensuring more comprehensive test coverage and improving test reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of the structure of a test case generation platform for aircraft performance testing provided in one embodiment of the present application;

[0052] Figure 2 A schematic structural diagram of a test case generation platform for aircraft performance testing provided in yet another embodiment of the present application;

[0053] Figure 3 A schematic structural diagram of a test case generation platform for aircraft performance testing provided in another embodiment of the present application;

[0054] Figure 4 A flowchart of a method for generating test cases for aircraft performance testing provided in one embodiment of the present application;

[0055] Description of reference numerals:

[0056] Test environment module 1, first generation unit 11, second generation unit 12, data exchange module 2, flight management module 3, simulation unit 31, flight management calculation unit 32, flight simulation calculation unit 33, first sensitivity analysis unit 13, second sensitivity analysis unit 14. DETAILED DESCRIPTION

[0057] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

[0058] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0059] In addition, the term "and / or" in this application is simply a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this application, unless otherwise specified, generally indicates that the related objects are in an "or" relationship.

[0060] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on the quantity and execution order.

[0061] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0062] When testing aircraft performance, test case design must consider various scenarios and objectives, such as attitude control, position control, navigation control, and flight mode switching. Furthermore, representative test cases must be designed based on the different functional modules of the aircraft management system to cover as many functional points and test requirements as possible. Different test parameters and test environments must also be considered to ensure the reliability and validity of test results.

[0063] The test cases are designed to simulate the complete flight scenario of a drone from takeoff to landing, in which certain components will fail in specific flight scenarios, such as landing gear failure during takeoff or landing or the operator continuously pulling the steering stick.

[0064] The test case data is divided into input information and output information, which is used to comprehensively evaluate the flight function and stability of the drone. The input data includes the test equipment and the modules and related data of the aircraft itself that need to be tested, including:

[0065] (1) Inertial Navigation System (INS) and its data: Provides 100 Hz and 400 Hz frequency inertial navigation data, providing information such as aircraft acceleration and angular velocity;

[0066] (2) GPS and DGPS and their data: GPS-1, GPS-2, GPS-3 and DGPS (differential GPS), a total of 4 modules, are used to provide accurate location information;

[0067] (3) Atmospheric Computer and its data: Two identical atmospheric computer devices are set up for redundant measurement. Device A and device B are both used to measure atmospheric parameters, such as temperature, air pressure, wind speed and other atmospheric data;

[0068] (4) Front wheel control device and its data: Two identical front wheel control devices are provided for redundant control, with device A and device B controlling the direction and position of the front wheels respectively;

[0069] (5) Power control equipment and its data: Two identical power control devices are provided for redundant control. Device A and device B are responsible for the management and control of the UAV power system respectively;

[0070] (6) Brake control device and its data: Two identical brake control devices are provided for redundant control, with device A and device B controlling the braking system of the UAV respectively;

[0071] (7) Controller primary and backup channels and their data: Controllers in each direction have primary and backup channels to ensure that the backup channel can take over control when the primary channel fails;

[0072] (8) Fiber optic attitude sensor and its data: Fiber optic attitude data with frequencies of 100 Hz and 400 Hz, providing aircraft attitude information;

[0073] (9) Operator commands and data: throttle levers, rudder control, etc.

[0074] Output data are key parameters generated during the flight of the drone, and are mainly used to evaluate flight stability and control performance. Output data generally includes:

[0075] (1) Flight attitude: such as pitch angle, roll angle, heading angle, etc.

[0076] (2) Flight data: including speed, altitude, climb rate, etc.

[0077] (3) Position coordinates: Real-time position information provided by GPS and DGPS is used to track the flight path.

[0078] (4) Track and heading information: used to record and analyze the flight path of the drone.

[0079] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0080] Reference Figure 1 An exemplary embodiment of the present application provides a test case generation platform for aircraft performance testing, which includes:

[0081] Test environment module 1, data exchange module 2 and flight management module 3;

[0082] Among them, the test environment module 1 includes a first generation unit 11 and a second generation unit 12; the test environment module 1 can automatically generate test cases according to the input parameter set, and drive the flight management module 3 to perform simulation testing according to the test cases, and then screen out test cases whose calculation results exceed the preset output reference range based on the simulation results of the flight management module 3 and the preset output reference range. The test cases include multiple input parameter sets.

[0083] Specifically, the first generation unit 11 is configured to determine a first input parameter set, generate a first test case based on the first input parameter set using a global random search, send the first test case to the flight management module 3 for simulation testing, and receive first flight status data from the flight management module 3. It should be noted that the test environment module 1 transmits the first test case generated by the first generation unit 11 in a specific format to the data exchange module 2, which then outputs it to the flight management module 3. After the flight management module 3 performs the simulation test, the obtained first flight status data is transmitted to the data exchange module 2, which then outputs it to the test environment module 1.

[0084] The second generation unit 12 is configured to determine a second input parameter set based on the first flight status data, generate a second test case based on the second input parameter set using a local random search, send the second test case to the flight management module 3 for simulation testing, receive the second flight status data from the flight management module 3, and determine the out-of-control input parameter based on the second flight status data. It should be noted that the test environment module 1 transmits the second test case generated by the second generation unit 12 in a specific format to the data exchange module 2, which then outputs it to the flight management module 3. After the flight management module 3 performs the simulation test, it transmits the obtained second flight status data to the data exchange module 2, which then outputs it to the test environment module 1.

[0085] The data exchange module 2 is used to cache the interaction data between the test environment module 1 and the flight management module 3, and the interaction data includes the first test case, the first flight status data, the second test case and the second flight status data.

[0086] The flight management module 3 is used to receive a first test case, perform a simulation test based on the first test case, and obtain first flight status data; receive a second test case, perform a simulation test based on the second test case, and obtain second flight status data.

[0087] It should be noted that the data exchange module 2 serves as a connection module between the test environment module 1 and the flight management module 3, and is used to cache massive first test cases and second test cases transmitted from the test environment module 1 to the flight management module 3, as well as first flight status data and second flight status data transmitted from the flight management module 3 to the test environment module 1. In order to facilitate better storage of massive interactive data, the data exchange module 2 stores data in a specific format.

[0088] Specifically, the data exchange module 2 stores input parameters in XML format, including variable names, upper and lower bounds, data types (continuous / discrete), bits, default values, data scaling ratios, and addresses; stores discriminant variables in XML format, including variable names, upper and lower bounds, and data types (continuous / discrete); stores variable relationships in XML format, broadly categorized as AND, OR, NOT, and SUBJECT, with each two variables in a broad category grouped together to reflect their relationships; and uses MAC and inp file formats to record test cases, with each test case being stored in a separate file for easy management and modification. Compared to the traditional method of manually adjusting input variables directly through reflective memory, the improved method achieves independent storage and automated management of test cases by recording test cases in MAC and inp file formats, improving efficiency and traceability and facilitating test case management and reuse.

[0089] In a preferred embodiment, the test environment module 1 and the flight management module 3 are deployed on a computing simulation server, and the test environment module 1 uses multi-process technology to enable or disable multiple flight management system software in the flight management module 3. The data exchange module is deployed on a data storage server. The simulation server and the data storage server are connected via a network protocol. Specifically, via one of the network protocols FC, iSCSI, NFS, CIFS, HTTP, and FTP. In response to the common problems in test case generation, such as large amount of calculation, difficulty in variable management, and difficulty in managing large amounts of data, this application proposes to use distributed computing and a hardware and software architecture driven by variable management to complete the generation of the use case environment, thereby achieving an efficient solution to this complex problem.

[0090] refer to Figure 2 In the test case generation platform for aircraft performance testing shown, the flight management module 3 includes a simulation unit 31 , a flight management calculation unit 32 and a flight simulation calculation unit 33 .

[0091] The simulation unit 31 includes multiple simulation systems, configured to receive a first test case from the data exchange module 2 and drive the multiple simulation systems to perform simulation tests using the first test case to obtain a first simulation result; and to receive a second test case from the data exchange module and drive the multiple simulation systems to perform simulation tests using the second test case to obtain a second simulation result. It should be noted that the simulation systems include ground station simulation systems, steering gear simulation systems, sensor simulation systems, cross-link simulation systems, and mission simulation systems. Simulation unit 31 is not limited to the aforementioned simulation systems.

[0092] The flight management calculation unit 32 is configured to adjust flight characteristic parameters based on simulation results and send the adjusted flight characteristic parameters to the flight simulation calculation unit 33. The simulation results include a first simulation result and a second simulation result. Specifically, the flight management calculation unit 32 receives the first simulation result from the simulation unit 31, makes a first adjustment to the flight characteristic parameters based on the first simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit 33. The flight management calculation unit 32 receives the second simulation result from the simulation unit 31, makes a second adjustment to the flight characteristic parameters based on the second simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit 33. The flight characteristic parameters include flight parameters and component parameters.

[0093] The flight simulation calculation unit 33 is configured to perform a comprehensive calculation on the adjusted flight characteristic parameters and the simulation results to obtain flight status data, which includes first flight status data and second flight status data. Specifically, the flight simulation calculation unit 33 performs a comprehensive calculation on the flight characteristic parameters after the first adjustment and the first simulation results to obtain the current first flight status data of the aircraft under test. Furthermore, the flight simulation calculation unit 33 performs a comprehensive calculation on the flight characteristic parameters after the second adjustment and the second simulation results to obtain the current second flight status data of the aircraft under test.

[0094] In some embodiments, when determining the first input parameter set and generating the first test case by using a global search based on the first input parameter set, the first generating unit 11 is specifically configured to:

[0095] Determining multiple input parameters of the aircraft under test in different flight mission phases and the value range corresponding to each input parameter, wherein the flight mission phase includes a takeoff phase, a flight phase, and a landing phase;

[0096] Randomly select two random values ​​within the value range of each input parameter, where the two random values ​​are located in the first half and the second half of the value range respectively;

[0097] All input parameters, corresponding random values ​​and different flight mission phases are combined to form the first test case.

[0098] It should be noted that when determining the random values ​​of all input parameters, the following constraints must be followed when combining them:

[0099] (a) If only one type of fault is considered, and three mission phases are required to perform combined changes, and the fault is considered to be recovered in the next phase, there are 2n*(3+3) possibilities, where 2n is a random value, one of which is 3 and does not recover in the next phase, and another is 3 and recovers in the next phase;

[0100] (b) Under the condition of two faults, three flight mission phases are required to perform combination changes, while considering the fault recovery in the next phase, including both faults recovering in the next phase, neither fault recovering in the next phase, and only one of the faults recovering in the next phase.

[0101] In a global random search, each iteration is independent and unaffected by the previous iteration. The value of each input variable is randomly selected within its range, and all variables are combined into a test case. During the global random search process, the test case is generated only once to roughly identify the problematic area.

[0102] In some embodiments, when the second generating unit 12 determines the second input parameter set based on the first flight status data and generates the second test case based on the second input parameter set using a local random search, it is specifically configured to:

[0103] The first flight status data is compared with the reference flight status data, and the potential fault input parameters, potential fault flight phase and potential fault value range of the abnormal flight of the aircraft under test are determined according to the comparison result; wherein the reference flight status data is used to indicate normal data.

[0104] The potential failure flight phase is subdivided. If the potential failure flight phase belongs to the take-off phase, the take-off phase is subdivided into the take-off phase and the climb phase. If the potential failure flight phase belongs to the flight phase, the flight phase is subdivided into the leveling phase, the cruising phase and the circling phase. If the potential failure flight phase belongs to the landing phase, the landing phase is subdivided into the approach phase and the landing phase.

[0105] The potential fault value interval is divided into four subintervals, and a random value is randomly determined in each subinterval;

[0106] The potential fault input parameters, potential fault flight phase and random values ​​are combined to generate a second test case.

[0107] In some preferred embodiments, the second generation unit 12 iterates the local random search process twice to accurately locate the problematic area. Local random search enhances global random search by adding iterative optimization capabilities. The candidate solutions for each iteration depend on the results of the previous iteration, and the search is concentrated near previously identified potential fault areas. This approach allows for more precise parameter adjustments and faster convergence to a parameter range with better performance.

[0108] In some preferred embodiments, in order to reduce the data size while ensuring the coverage of test cases, refer to Figure 3 , the test environment module 1 further includes a first sensitivity analysis unit 13;

[0109] Specifically, the first sensitivity analysis unit 13 is used to use the gradient method to analyze the degree of influence of each input parameter in the first test case on the output result; determine the logical relationship between each input parameter based on the degree of influence, where the logical relationship includes AND, OR, NOT and subordination; based on the logical relationship, identify redundant test cases in the first test case and delete the redundant test cases.

[0110] The gradient method calculates the partial derivative of the objective function with respect to each input parameter, determining the change in output when each input parameter changes. By observing whether a certain quantity in the flight state of the component after entering the simulation test exceeds a certain limit, a sensitivity analysis of the input and output parameters is performed to assist in determining the accuracy of the positioning area. If two input parameters cause the output parameter to change, they are considered to have an AND or OR dependency relationship.

[0111] After determining the logical relationship between the various input parameters, determine the redundant test cases according to the following rules:

[0112] Through the AND relationship, the values ​​of multiple input parameters can be combined together without considering all possible values ​​of each input parameter separately. They only need to be combined into one test case, and the rest are used as redundant test cases, thereby reducing the number of independently generated test cases.

[0113] When considering test cases, the number of combinations that must be considered can be reduced. As long as one of the conditions is met, both conditions do not need to be met at the same time, thus significantly reducing the number of test cases.

[0114] By clarifying which combinations are unnecessary and excluding certain value combinations, the size of the test cases can be reduced, further narrowing the scope of generated test cases. By reducing the number of freely combinable options, dependency relationships can significantly reduce the number of test cases required. By capturing dependencies between input parameters, invalid or impossible combinations can be avoided, reducing redundancy.

[0115] In some preferred embodiments, continue to refer to Figure 3 The test environment module 1 also includes a second sensitivity analysis unit 14; the second sensitivity analysis unit 14 is used to perform dimensionality reduction processing on the second test case using the trained random forest model. Specifically, the random forest classifier in the sklearn.ensemble toolkit is used to train the simulation data after local random search. The random forest classifier has a built-in feature importance scoring mechanism, and the importance score of each input parameter can be obtained through the feature_importances_ attribute; further, based on the feature importance score, the top N most important features are selected as new input features, and unimportant features are removed to complete the dimensionality reduction. N is selected as the more important 75% of all variable values.

[0116] In a preferred embodiment, when the first generating unit sends the first test case to the flight management module for simulation testing, it is specifically used to:

[0117] Set preset simulation times for multiple flight mission stages, and generate test cases for each flight mission stage according to the preset simulation time; send the test cases for each flight mission stage to the flight management module through a multi-process method, and drive the flight management module to complete the simulation test of the entire process.

[0118] The test case generation platform of this application supports massive test environments, and has the characteristics of large quantity and comprehensive coverage. Compared with traditional manually generated test cases, it can ensure the comprehensiveness of test cases and improve generation efficiency; the test environment module provides an optimized search algorithm from global to local, avoiding the problem that traditional raster search algorithms easily miss unqualified cases; through sensitivity analysis, the relationship between components and components, and components and flight status in the flight management module is improved, and while ensuring coverage, the scale of test case generation is effectively reduced, and efficient testing is carried out on related components, thereby improving the pertinence and efficiency of testing.

[0119] Another embodiment of the present application also provides a test case generation method for aircraft performance testing, referring to Figure 4 As shown, the method includes:

[0120] S1: Determine a first input parameter set, and generate a first test case based on the first input parameter set using a global random search;

[0121] S2: Sending the first test case to the flight management module, driving the flight management module to perform a simulation test, and receiving first flight status data output by the flight management module after the simulation test;

[0122] S3: determining a second input parameter set based on the first flight state information, and generating a second test case based on the second input parameter set by using a local random search;

[0123] S4: Sending the second test case to the flight management module, driving the flight management module to perform a simulation test, and receiving second flight status data output by the flight management module after the simulation test;

[0124] S5: Determine an out-of-control input parameter based on the second flight status data.

[0125] Wherein, step S1 specifically includes:

[0126] S11: Determine multiple input parameters of the aircraft under test in each flight mission phase and a value range corresponding to each input parameter, wherein the flight mission phase includes a take-off phase, a flight phase, and a landing phase;

[0127] S12: Randomly select two random values ​​within the value range of each input parameter, where the two random values ​​are located in the first half and the second half of the value range respectively;

[0128] S13: Combine all input parameters, corresponding random values ​​and different flight mission phases to form a first test case.

[0129] The flight management module includes a simulation unit, a flight management calculation unit, and a flight simulation calculation unit. Step S2 sends the first test case to the flight management module to drive the flight management module to perform a simulation test, specifically including:

[0130] S21: Using a first test case to drive multiple simulation systems in a simulation unit to perform simulation testing to obtain a first simulation result;

[0131] S22: The flight management calculation unit adjusts the flight characteristic parameters based on the first simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit;

[0132] S23: The flight simulation calculation unit performs comprehensive calculation on the adjusted flight characteristic parameters and the first simulation result to obtain first flight status data.

[0133] In step S3, determining a second input parameter set based on the first flight status information, and generating a second test case based on the second input parameter set using a local random search include:

[0134] S31: comparing the first flight state data with the reference flight state data, and determining, based on the comparison result, potential fault input parameters, potential fault flight phase, and potential fault value range for the abnormal flight of the aircraft under test;

[0135] S32: The potential failure flight phases are subdivided into the takeoff phase and the climb phase, the flight phase is subdivided into the leveling phase, the cruising phase and the circling phase, and the landing phase is subdivided into the approach phase and the landing phase;

[0136] S33: Divide the potential fault value interval into four subintervals, and randomly determine a random input value in each subinterval;

[0137] The potential fault input parameters, potential fault flight phase and random input values ​​are combined to generate a second test case.

[0138] The flight management module includes a simulation unit, a flight management calculation unit, and a flight simulation calculation unit. In step S4, the second test case is sent to the flight management module to drive the flight management module to perform a simulation test, including:

[0139] S41: Using a second test case to drive multiple simulation systems in the simulation unit to perform simulation testing to obtain a second simulation result;

[0140] S42: The flight management calculation unit adjusts the flight characteristic parameters based on the second simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit;

[0141] S43: The flight simulation calculation unit performs comprehensive calculation on the adjusted flight characteristic parameters and the second simulation result to obtain second flight state data.

[0142] In some embodiments, step S1 further includes:

[0143] Perform sensitivity analysis on the first test case;

[0144] Using the gradient method to analyze the influence of each input parameter on the output result in the first test case;

[0145] Determine the logical relationship between various input parameters based on the degree of influence, wherein the logical relationship includes AND, OR, NOT and SUBJECT;

[0146] Based on the logical relationship, redundant test cases in the first test case are identified and deleted.

[0147] In some embodiments, step S4 further includes: performing dimensionality reduction processing on the second test case using the trained random forest model.

[0148] An embodiment of the present application provides a computer device, which may include a processor, memory, a network interface, and a database connected via a system bus. The processor of the computer device is configured 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, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the processor executes the steps of the test case generation method for aircraft performance testing as described in any of the above embodiments.

[0149] The working process, working details and technical effects of the computer equipment provided in this embodiment can be found in the embodiment of the test case generation method for aircraft performance testing described above, and will not be described in detail here.

[0150] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, the division of the above-mentioned functional units and modules is only used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system described in this application is divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A test case generation method for aircraft performance testing, characterized in that: include: Determining a first input parameter set, and generating a first test case based on the first input parameter set using a global random search, wherein a plurality of input parameters and a value range corresponding to each input parameter for the aircraft under test in each flight mission phase are determined, wherein the flight mission phase includes a takeoff phase, a flight phase, and a landing phase; randomly selecting two random values ​​within the value range of each input parameter, wherein the two random values ​​are respectively located in the first half and the second half of the value range; and combining all input parameters, corresponding random values, and different flight mission phases to generate a first test case; Sending the first test case to a flight management module, driving the flight management module to perform a simulation test, and receiving first flight status data output by the flight management module after the simulation test; Determine a second input parameter set based on the first flight status data, and generate a second test case based on the second input parameter set using a local random search, wherein the first flight status data is compared with reference flight status data, and based on the comparison result, determine potential fault input parameters, potential fault flight phases, and potential fault value ranges for a flight abnormality of the aircraft under test, and subdivide the potential fault flight phases, wherein the takeoff phase is subdivided into a takeoff and liftoff phase and a climb phase, the flight phase is subdivided into a leveling phase, a cruising phase, and a circling phase, and the landing phase is subdivided into an approach phase and a landing phase. The potential fault value range is equally divided into four sub-intervals, and a random input value is randomly determined in each sub-interval. The potential fault input parameters, potential fault flight phases, and random input values ​​are combined to generate a second test case. Sending the second test case to a flight management module, driving the flight management module to perform a simulation test, and receiving second flight status data output by the flight management module after the simulation test; A loss of control input parameter is determined based on the second flight state data.

2. The test case generation method for aircraft performance testing according to claim 1, characterized in that: The flight management module includes a simulation unit, a flight management calculation unit, and a flight simulation calculation unit. The first test case is sent to the flight management module, and the flight management module is driven to perform a simulation test, including: Using the first test case to drive multiple simulation systems in the simulation unit to perform simulation testing to obtain a first simulation result; The flight management calculation unit adjusts the flight characteristic parameters based on the first simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit; The flight simulation calculation unit performs comprehensive calculation on the adjusted flight characteristic parameters and the first simulation result to obtain first flight status data.

3. The test case generation method for aircraft performance testing according to claim 1, characterized in that: The flight management module includes a simulation unit, a flight management calculation unit, and a flight simulation calculation unit. The second test case is sent to the flight management module, and the flight management module is driven to perform a simulation test, including: Using the second test case to drive multiple simulation systems in the simulation unit to perform simulation testing to obtain a second simulation result; The flight management calculation unit adjusts the flight characteristic parameters based on the second simulation result, and sends the adjusted flight characteristic parameters to the flight simulation calculation unit; The flight simulation calculation unit performs comprehensive calculation on the adjusted flight characteristic parameters and the second simulation result to obtain second flight status data.

4. The test case generation method for aircraft performance testing according to claim 1, characterized in that: After generating the first test case, the method further includes: performing a sensitivity analysis on the first test case; Using the gradient method to analyze the influence of each input parameter on the output result in the first test case; Determine a logical relationship between the input parameters based on the influence degree, wherein the logical relationship includes AND, OR, NOT and SUBJECT; Based on the logical relationship, redundant test cases in the first test cases are identified and the redundant test cases are deleted.

5. The test case generation method for aircraft performance testing according to claim 4, characterized in that: After generating the second test case, it also includes: Used to perform dimensionality reduction on the second test case using the trained random forest model.

6. A test case generation platform for aircraft performance testing, characterized in that: Used to perform the method according to any one of claims 1 to 5, comprising: Test environment module, data exchange module and flight management module; The test environment module includes a first generation unit and a second generation unit, wherein the first generation unit is used to determine a first input parameter set, generate a first test case based on the first input parameter set using a global random search, send the first test case to a flight management module for simulation testing, and receive first flight status data of the flight management module; the second generation unit is used to determine a second input parameter set based on the first flight status data, generate a second test case based on the second input parameter set using a local random search, send the second test case to the flight management module for simulation testing, receive second flight status data of the flight management module, and determine an out-of-control input parameter based on the second flight status data; The first generation unit is used to determine multiple input parameters of the aircraft under test in each flight mission phase and the corresponding value range of each input parameter, wherein the flight mission phase includes the takeoff phase, the flight phase, and the landing phase; randomly select two random values ​​within the value range of each input parameter, wherein the two random values ​​are respectively located in the first half and the second half of the value range; and combine all input parameters, corresponding random values, and different flight mission phases to generate a first test case; a second generating unit, configured to compare the first flight status data with reference flight status data, determine potential fault input parameters, potential fault flight phases, and potential fault value intervals for a test aircraft flight abnormality based on the comparison results, subdivide the potential fault flight phases, wherein the takeoff phase is subdivided into a takeoff and liftoff phase and a climb phase, the flight phase is subdivided into a leveling phase, a cruising phase, and a circling phase, and the landing phase is subdivided into an approach phase and a landing phase, divide the potential fault value interval into four sub-intervals, randomly determine a random input value in each sub-interval, and combine the potential fault input parameters, the potential fault flight phase, and the random input value to generate a second test case; a data exchange module, configured to cache interaction data between the test environment module and the flight management module, the interaction data including a first test case, first flight status data, a second test case, and second flight status data; The flight management module is used to receive the first test case, perform simulation testing based on the first test case, and obtain first flight status data; receive the second test case, perform simulation testing based on the second test case, and obtain second flight status data.

7. The test case generation platform for aircraft performance testing according to claim 6, characterized in that: The test environment module further includes a first sensitivity analysis unit and a second sensitivity analysis unit; The first sensitivity analysis unit is configured to analyze the degree of influence of each input parameter in the first test case on the output result using a gradient method, determine a logical relationship between the input parameters based on the degree of influence, wherein the logical relationship includes AND, OR, NOT, and dependency, identify redundant test cases in the first test case based on the logical relationship, and delete the redundant test cases; The second sensitivity analysis unit is used to perform dimensionality reduction processing on the second test case using the trained random forest model.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

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