A method and device for simulating atmospheric environment in aerospace flight simulation

Abstract

The present invention discloses an atmospheric environment simulation method and device for aerospace flight simulation, which relates to data processing related fields, including: constructing a multi-stage environment for a flight test plan of an aerospace aircraft according to an atmospheric data system tester to obtain a multi-stage flight simulation environment; conducting a simulation test on the aerospace aircraft to obtain a first test sequence; performing simulation scene variation compensation to obtain a second test sequence; conducting multi-dimensional risk prediction on the test plan to obtain a multi-stage test risk prediction result; performing sensitivity correlation to obtain a multi-order risk environment sensitivity factor and optimizing the test plan. The method solves the technical problems that the environmental simulation scene limitations and the lack of uncertainty factor considerations in the adaptability tests of existing aerospace aircraft in different atmospheric environments lead to incomplete and inaccurate test results, and achieves the technical effect of truly and comprehensively simulating the flight state of the aerospace aircraft in a complex atmospheric environment and improving the comprehensiveness and accuracy of the test results.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a method and device for simulating the atmospheric environment for aerospace flight simulation. Background Art

[0002] In the field of aerospace vehicle research and development and testing, ensuring the safe and reliable operation of the vehicle in a complex and changeable real atmospheric environment is of vital importance. This is directly related to the success or failure of aerospace missions and the safety of the vehicle and personnel. At present, the main method to solve the problem of adaptability testing of aerospace vehicles in different atmospheric environments is to construct relatively fixed atmospheric environment simulation scenarios and conduct routine simulation tests on the vehicle based on these preset scenarios. However, due to the relatively simple environmental simulation scenarios constructed by the current method, lack of dynamic changes, and the failure to fully consider the various uncertainties that may arise during the simulation test process, the test results are difficult to fully and accurately reflect the performance and potential risks of the aircraft in the actual complex atmospheric environment, and cannot provide a sufficiently effective optimization basis for the flight test plan.

[0003] In the current relevant technologies, the adaptability testing of aerospace vehicles in different atmospheric environments has limitations in environmental simulation scenarios and lacks consideration of uncertainty factors, resulting in technical problems such as incomplete and inaccurate test results. Summary of the Invention

[0004] The present application provides an atmospheric environment simulation method and device for aerospace flight simulation, adopts a multi-stage flight simulation environment constructed by an atmospheric data system tester according to a flight test plan, conducts tests in the simulation environment, generates a first sequence of flight simulation tests, compensates for environmental variations in the first test sequence, generates a second sequence, conducts multi-dimensional risk prediction based on the second sequence, generates risk prediction results, analyzes the risk prediction results, identifies sensitive factors, optimizes the flight test plan and other technical means, thereby solving the technical problems of incomplete and inaccurate test results caused by the limitations of environmental simulation scenarios and the lack of uncertainty factor considerations in the adaptability tests of existing aerospace vehicles in different atmospheric environments, and achieving the technical effect of truly and comprehensively simulating the flight state of aerospace vehicles in complex atmospheric environments and improving the comprehensiveness and accuracy of test results.

[0005] The present application provides an atmospheric environment simulation method for aerospace flight simulation, comprising: constructing a multi-stage environment for a flight test plan of an aerospace vehicle according to an atmospheric data system tester to obtain a multi-stage flight simulation environment; based on the flight test plan, performing a simulation test on the aerospace vehicle according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; performing simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests; based on a flight test risk prediction factor, performing multi-dimensional risk prediction on the flight test plan according to the second sequence of flight simulation tests to obtain a multi-stage test risk prediction result; performing sensitivity association on the multi-stage flight simulation environment according to the multi-stage test risk prediction result to obtain a multi-order risk environment sensitivity factor, and adaptively optimizing the flight test plan according to the multi-order risk environment sensitivity factor.

[0006] In a possible implementation, based on the flight test plan, the aerospace vehicle is simulated and tested according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests, and the following processing is performed: modeling is performed according to the aerospace vehicle to obtain an aircraft model; multi-stage test importance evaluation is performed according to the flight test plan to obtain the importance of each stage test, and a multi-stage simulation test confidence mechanism is constructed based on the importance of each stage test; based on the multi-stage simulation test confidence mechanism, multi-stage tests are performed on the aircraft model according to the flight test plan and the multi-stage flight simulation environment to obtain a multi-stage test data set; confidence fusion is performed based on the multi-stage test data set to generate the first sequence of flight simulation tests.

[0007] In a possible implementation, simulation scenario variation compensation is performed on the first sequence of the flight simulation test according to the multi-stage flight simulation environment to obtain a second sequence of the flight simulation test, and the following processing is performed: simulation test scenario information is collected according to the first sequence of the flight simulation test to obtain a multi-stage simulation test scenario; variation identification is performed on the multi-stage simulation test scenario according to the multi-stage flight simulation environment to obtain environmental variation identification results for each stage; impact analysis is performed on the first sequence of the flight simulation test according to the environmental variation identification results for each stage to obtain variation impact analysis results for each stage; the first sequence of the flight simulation test is corrected according to the variation impact analysis results for each stage to generate the second sequence of the flight simulation test.

[0008] In a possible implementation, based on the flight test risk prediction factor, the flight test plan is subjected to multi-dimensional risk prediction according to the second sequence of the flight simulation test to obtain a multi-stage test risk prediction result, and the following processing is performed: according to the second sequence of the flight simulation test, a multi-stage simulation data block is constructed, and the multi-stage simulation data block includes a takeoff simulation data block, a climb simulation data block, a cruise simulation data block, a re-entry simulation data block and a landing simulation data block; the flight test risk prediction factor includes a power instability risk index, a structural safety risk index and an attitude deviation risk index; based on the flight test risk prediction factor, the flight test plan is subjected to multi-stage risk prediction according to the multi-stage simulation data block to generate the multi-stage test risk prediction result, and the multi-stage test risk prediction result includes a takeoff test risk prediction result, a climb test risk prediction result, a cruise test risk prediction result, a re-entry test risk prediction result and a landing test risk prediction result.

[0009] In a possible implementation, based on the flight test risk prediction factor, a multi-stage risk prediction is performed on the flight test plan according to the multi-stage simulation data block, and the following processing is performed: thrust characteristics are identified according to the takeoff simulation data block to obtain a takeoff thrust characteristic sequence; speed characteristics are identified on the takeoff simulation data block to obtain a takeoff speed characteristic sequence; the takeoff thrust characteristic sequence and the takeoff speed characteristic sequence are aligned to obtain a takeoff thrust-speed characteristic sequence; normal takeoff thrust-speed samples are retrieved according to the takeoff phase test plan to construct a takeoff thrust-speed reference sequence; power instability risk detection is performed on the takeoff thrust-speed characteristic sequence according to the takeoff thrust-speed reference sequence to obtain a takeoff power instability risk prediction result, and the takeoff power instability risk prediction result is added to the takeoff test risk prediction result.

[0010] In a possible implementation, based on the flight test risk prediction factor, the flight test plan is subjected to multi-stage risk prediction according to the multi-stage simulation data block, and the following processing is performed: according to the takeoff simulation data block, the takeoff process pressure simulation data and the takeoff attitude simulation data are read; according to the takeoff process pressure simulation data, multi-structure safety risk prediction is performed on the aerospace vehicle to obtain a takeoff structure safety risk prediction result; normal attitude fitting is performed according to the takeoff stage test plan to determine the takeoff reference attitude data; the takeoff attitude simulation data and the takeoff reference attitude data are input into the attitude deviation risk prediction model to obtain a takeoff attitude deviation risk prediction result; the takeoff structure safety risk prediction result and the takeoff attitude deviation risk prediction result are added to the takeoff test risk prediction result.

[0011] In a possible implementation, the multi-stage flight simulation environment is sensitivity-associated according to the multi-stage test risk prediction results to obtain a multi-order risk environment sensitivity factor, and the following processing is performed: the multi-stage test risk prediction results are element-associated according to the multi-stage flight simulation environment to obtain a multi-stage risk-associated environmental element; the multi-stage test risk prediction results are subjected to a disturbance impact evaluation according to the multi-stage risk-associated environmental element to obtain a disturbance evaluation result of each environmental element; the multi-stage risk-associated environmental element is optimized according to the disturbance evaluation result of each environmental element to generate the multi-order risk environment sensitivity factor.

[0012] In a possible implementation, a multi-stage environment is constructed for the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment, and the following processing is performed: the flight stages are disassembled according to the flight test plan to obtain a multi-stage flight test plan, and the multi-stage flight test plan includes a take-off stage test plan, a climb stage test plan, a cruise stage test plan, a re-entry stage test plan and a landing stage test plan; the environment is predicted according to the multi-stage flight test plan to obtain a multi-stage flight prediction environment; based on the multi-stage flight prediction environment, the multi-stage flight simulation environment is constructed according to the atmospheric data system tester.

[0013] In a possible implementation, based on the multi-stage flight prediction environment and according to the atmospheric data system tester, the multi-stage flight simulation environment is constructed, and the following processing is performed: based on the multi-stage flight prediction environment and according to the atmospheric data system tester, a multi-stage flight construction environment is obtained; the multi-stage flight prediction environment and the multi-stage flight construction environment are dynamically compared to obtain a multi-stage environment comparison result, and the multi-stage flight construction environment is corrected according to the multi-stage environment comparison result to obtain the multi-stage flight simulation environment.

[0014] The present application also provides an atmospheric environment simulation device for aerospace flight simulation, including: a multi-stage environment construction module, which is used to construct a multi-stage environment for the flight test plan of the aerospace vehicle according to the atmospheric data system tester, and obtain a multi-stage flight simulation environment; a simulation test module, which is used to perform simulation testing on the aerospace vehicle based on the flight test plan and the multi-stage flight simulation environment, and obtain a first sequence of flight simulation tests; a simulation scenario variation compensation module, which is used to perform simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment, and obtain a second sequence of flight simulation tests; a multi-dimensional risk prediction module, which is used to perform multi-dimensional risk prediction on the flight test plan based on the flight test risk prediction factor and the second sequence of flight simulation tests, and obtain a multi-stage test risk prediction result; a sensitivity association module, which is used to perform sensitivity association on the multi-stage flight simulation environment according to the multi-stage test risk prediction result, and obtain a multi-order risk environment sensitivity factor, and adaptively optimize the flight test plan according to the multi-order risk environment sensitivity factor.

[0015] The atmospheric environment simulation method and device for aerospace flight simulation proposed in this application first constructs a multi-stage environment for the flight test plan of the aerospace aircraft according to the atmospheric data system tester to obtain a multi-stage flight simulation environment. Then, based on the flight test plan, the aerospace aircraft is simulated and tested according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests. Then, the first sequence of flight simulation tests is compensated for simulation scenario variations according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests. Then, based on the flight test risk prediction factor, the flight test plan is multi-dimensionally predicted for risk according to the second sequence of flight simulation tests to obtain a multi-stage test risk prediction result. Finally, the multi-stage flight simulation environment is sensitivity-correlated according to the multi-stage test risk prediction result to obtain a multi-order risk environment sensitivity factor. The flight test plan is adaptively optimized according to the multi-order risk environment sensitivity factor. This achieves the technical effect of realistically and comprehensively simulating the flight state of the aerospace aircraft in a complex atmospheric environment and improving the comprehensiveness and accuracy of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact sequence. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flow chart of an atmospheric environment simulation method for aerospace flight simulation provided in an embodiment of the present application.

[0018] Figure 2 A schematic structural diagram of an atmospheric environment simulation device for aerospace flight simulation provided in an embodiment of the present application.

[0019] Explanation of the accompanying drawings: multi-stage environment construction module 10, simulation test module 20, simulation scenario variation compensation module 30, multi-dimensional risk prediction module 40, sensitivity association module 50. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms “first\second” involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application provides a method for simulating the atmospheric environment of aerospace flight simulation. Figure 1 As shown, the method includes:

[0024] Step S100: construct a multi-stage environment for the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment.

[0025] Specifically, according to the flight test plan, the entire flight process is broken down into multiple phases, such as takeoff, climb, cruise, reentry, and landing. Atmospheric parameters (such as temperature, pressure, wind speed, and humidity) are acquired for each phase using an Atmospheric Data System (ADS) instrument (an instrument used to measure atmospheric parameters, installed on the aircraft or at a ground-based weather station). These parameters are then combined with meteorological models to predict the atmospheric environment for each phase. A multi-phase flight simulation environment is constructed using computer simulation software (such as MATLAB / Simulink and ANSYS Fluent). The predicted atmospheric parameters are then input into the simulation model to generate a virtual atmospheric environment for each phase.

[0026] For example, during takeoff, the ADS instrument measures the temperature at the takeoff airport to be 25°C, the air pressure to be 1013hPa, and the wind speed to be 5m / s. These parameters are input into the simulation software to construct the takeoff atmospheric environment. During the cruise phase, the ADS instrument, combined with meteorological satellite data, predicts the atmospheric temperature at a cruising altitude (10km) to be -50°C, the air pressure to be 260hPa, and the wind speed to be 100m / s. These parameters are then input into the simulation model to generate the cruise environment.

[0027] In one possible implementation, a multi-stage environment is constructed for the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment. Step S100 further includes step S110, in which the flight stages are disassembled according to the flight test plan to obtain a multi-stage flight test plan. The multi-stage flight test plan includes a take-off stage test plan, a climb stage test plan, a cruise stage test plan, a re-entry stage test plan, and a landing stage test plan. Specifically, the flight test plan of the aerospace vehicle is analyzed in detail to clarify the key stages of the flight process. The entire flight process is divided into multiple stages, including the take-off stage, the climb stage, the cruise stage, the re-entry stage, and the landing stage. Each stage has its own specific flight mission and environmental conditions. A detailed test plan is formulated for each stage, including test objectives, test parameters, test environment requirements, etc. For example, the take-off phase test plan tests the aircraft's engine thrust, take-off speed, attitude control and other parameters; the climb phase test plan tests the aircraft's climb rate, fuel consumption, aerodynamic performance and other parameters; the cruise phase test plan tests the aircraft's range, fuel efficiency, flight altitude stability and other parameters; the re-entry phase test plan tests the aircraft's thermal protection performance, aerodynamic deceleration performance and other parameters when re-entering the atmosphere; and the landing phase test plan tests the aircraft's landing speed, landing attitude, braking performance and other parameters.

[0028] Step S120 performs an environmental prediction based on the multi-stage flight test plan to obtain a multi-stage predicted flight environment. Specifically, an atmospheric data system (ADS) is used to obtain atmospheric parameters for the current and future periods, such as temperature, pressure, wind speed, and humidity. Meteorological models (such as global and local weather models) are then used to predict the atmospheric environment for each stage. Based on the prediction results, a predicted flight environment for each stage is generated. For example, during takeoff, the ADS measures the temperature at the takeoff airport as 25°C, the air pressure as 1013hPa, and the wind speed as 5m / s. The meteorological model is then used to predict the atmospheric environment for takeoff within the next hour. During the cruise phase, the ADS, combined with meteorological satellite data, predicts the atmospheric temperature at the cruise altitude (10km) to be -50°C, the air pressure to be 260hPa, and the wind speed to be 100m / s, generating the predicted cruise environment.

[0029] Step S130: Based on the multi-stage flight prediction environment and the atmospheric data system tester, the multi-stage flight simulation environment is constructed. Specifically, the multi-stage flight prediction environment parameters generated in step S120 are input into computer simulation software, and the simulation software (such as MATLAB / Simulink or ANSYS Fluent) is used to construct a virtual atmospheric environment for each stage. The constructed virtual environment is calibrated to ensure its consistency with the actual predicted environment. For example, for the takeoff phase, the temperature, air pressure, wind speed and other parameters measured by the ADS tester are input into MATLAB / Simulink to construct the virtual atmospheric environment for the takeoff phase. For the cruise phase, the predicted temperature, air pressure, wind speed and other parameters are input into ANSYS Fluent to generate the virtual atmospheric environment for the cruise phase.

[0030] This approach divides the flight process into multiple phases and develops detailed test plans for each phase, enabling more comprehensive testing of aircraft performance in different flight phases. Furthermore, by combining ADS testers with weather models to predict the environment, the actual flight environment can be more accurately simulated, enhancing the comprehensiveness and accuracy of the test.

[0031] In one possible implementation, the multi-stage flight simulation environment is constructed based on the multi-stage flight prediction environment and the air data system tester. Step S130 further includes step S131: obtaining a multi-stage flight simulation environment based on the multi-stage flight prediction environment and the air data system tester. Specifically, based on the multi-stage flight prediction environment, the air data system tester (ADS) is used to collect current environmental parameters, including temperature, pressure, wind speed, and humidity. The collected environmental parameters are input into computer simulation software (such as MATLAB / Simulink or ANSYS Fluent) to preliminarily construct the flight environment for each stage, generating a multi-stage flight simulation environment that includes virtual environments for takeoff, climb, cruise, reentry, and landing.

[0032] For example, during takeoff, the ADS tester measured a temperature of 25°C, an air pressure of 1013 hPa, and a wind speed of 5 m / s at the takeoff airport. These parameters were entered into MATLAB / Simulink to initially construct a virtual atmosphere for takeoff. During the cruise phase, the ADS tester combined meteorological satellite data to predict an atmospheric temperature of -50°C, an air pressure of 260 hPa, and a wind speed of 100 m / s at a cruising altitude (10 km). These parameters were then entered into ANSYS Fluent to generate a virtual atmosphere for the cruise phase.

[0033] Step S132 dynamically compares the multi-stage flight prediction environment with the multi-stage flight construction environment to obtain a multi-stage environment comparison result. The multi-stage flight construction environment is then corrected based on the multi-stage environment comparison result to obtain the multi-stage flight simulation environment. Specifically, the multi-stage flight prediction environment and the multi-stage flight construction environment are compared in real time, including key parameters such as temperature, pressure, wind speed, and humidity. An error analysis algorithm (such as root mean square error (RMSE)) is used to detect the deviation between the prediction environment and the construction environment. Based on the comparison results, the multi-stage flight construction environment is corrected using methods such as parameter adjustment and model modification to ensure consistency between the construction environment and the prediction environment. The corrected environment becomes the multi-stage flight simulation environment and is used for subsequent aircraft testing.

[0034] This implementation ensures the consistency between the multi-stage flight constructed environment and the predicted environment through dynamic comparison and correction, thereby improving the accuracy of the simulated environment and helping to more realistically reflect the environmental conditions that the aircraft may encounter in actual flight.

[0035] Step S200: Based on the flight test plan, the aerospace vehicle is simulated tested according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests.

[0036] Specifically, computer-aided design (CAD) software (such as SolidWorks and CATIA) and simulation software (such as MATLAB / Simulink) are used to model the aerospace vehicle and generate a digital model of the vehicle. Based on the flight test plan, a weight allocation algorithm (such as the Analytic Hierarchy Process (AHP)) is used to evaluate the importance of each test phase and generate the test importance for each phase. Based on the test importance of each phase, a multi-stage simulation test confidence mechanism is generated using the Monte Carlo method to ensure test reliability. In a multi-stage flight simulation environment, the aircraft model undergoes multi-stage testing, and the test data (such as velocity, acceleration, attitude angle, etc.) is recorded for each phase to generate a multi-stage test dataset. Data fusion algorithms (such as Kalman filtering) are used to fuse the multi-stage test datasets to generate the first sequence of flight simulation tests.

[0037] For example, SolidWorks is used to build a 3D model of the aircraft, including components such as the wings, fuselage, and engine. The aircraft's dynamics model, including aerodynamics and propulsion, is then built in MATLAB / Simulink. During takeoff, data such as the aircraft's thrust, speed, and attitude angle are recorded; during cruise, data such as lift, drag, and fuel consumption are recorded.

[0038] In one possible implementation, based on the flight test plan, the aerospace vehicle is simulated and tested in the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests. Step S200 further includes step S210, in which the aerospace vehicle is modeled to obtain an aircraft model. Specifically, a three-dimensional geometric model of the aerospace vehicle is created using computer-aided design (CAD) software (such as SolidWorks or CATIA), including components such as wings, fuselage, and engines. Physical properties such as mass distribution, aerodynamic characteristics, and propulsion systems are added to the aircraft model in simulation software (such as MATLAB / Simulink or ANSYS Fluent). A flight control system model is added to the aircraft model, including modules such as attitude control and velocity control.

[0039] For example, create a 3D aircraft model in SolidWorks, including detailed structures of the wings, fuselage, and engine. Add aerodynamic parameters, such as lift coefficient and drag coefficient, to the aircraft model in MATLAB / Simulink. Add an attitude control algorithm to the aircraft model to ensure it maintains a stable flight attitude during simulation tests.

[0040] Step S220 evaluates the importance of multiple test phases based on the flight test plan, determines the importance of each phase, and constructs a multi-phase simulation test confidence mechanism based on the importance of each phase. Specifically, based on the flight test plan, the Analytic Hierarchy Process (AHP) or other weighting algorithm is used to evaluate the importance of each phase test, such as takeoff, climb, cruise, reentry, and landing, and assign weights. Based on the importance of each phase test, the Monte Carlo method is combined to determine the number of simulation tests for each phase, and the multi-phase simulation test confidence mechanism is constructed.

[0041] For example, through AHP analysis, we determined that the importance of takeoff testing is 0.3, climb testing is 0.2, cruise testing is 0.3, reentry testing is 0.1, and landing testing is 0.1. Based on the weights, we determine the number of simulation tests for each phase. For example, we might test 15 times for takeoff, 10 times for climb, 15 times for cruise, 5 times for reentry, and 5 times for landing.

[0042] Step S230: Based on the multi-stage simulation test confidence mechanism, the aircraft model is subjected to multi-stage testing according to the flight test plan and the multi-stage flight simulation environment to obtain a multi-stage test dataset. Specifically, corresponding test environment parameters are set for each stage based on the multi-stage flight simulation environment. The aircraft model is run in the simulation software, and multi-stage testing is performed according to the predetermined test plan and environmental parameters. Test data for each stage is recorded, including speed, acceleration, attitude angle, fuel consumption, etc. The test data is collected using a data acquisition system (such as the data recording module in MATLAB / Simulink) to generate a multi-stage test dataset. For example, test data from 15 takeoff stages is collected to generate a takeoff test dataset.

[0043] Step S240: Confidence fusion is performed on the multi-stage test data set to generate the first sequence of flight simulation tests. Specifically, the multi-stage test data set is preprocessed to remove outliers and noise. Confidence fusion is performed on the test data for each stage based on the number of tests. For example, the mean of the test data for each stage is taken to generate the first sequence of flight simulation tests. For example, the test data from 15 takeoff stages is preprocessed to remove outliers, and the mean of the takeoff stage test data is calculated to generate the takeoff stage test results.

[0044] This implementation method allocates the number of tests according to the test importance of each stage, which can reasonably allocate test resources, avoid resource waste, and improve test efficiency.

[0045] Step S300 : performing simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests.

[0046] Specifically, simulation scenario variation compensation refers to correcting environmental variations (such as wind speed changes and air pressure fluctuations) in the simulated test scenario to more closely resemble the actual flight environment. A sensor network (such as an IMU and pressure sensor) is used to collect scenario information from the first sequence of flight simulation tests to generate a multi-stage simulation test scenario. Machine learning algorithms (such as support vector machines (SVMs) and neural networks) are used to identify variations in the multi-stage simulation test scenario, generating environmental variation identification results for each stage. Causal analysis algorithms (such as Bayesian networks) are used to analyze the impact of environmental variation identification results for each stage, generating analysis results for the impact of each stage. Based on the analysis results for each stage, interpolation algorithms (such as linear interpolation and spline interpolation) are used to correct the first sequence of flight simulation tests to generate a second sequence of flight simulation tests.

[0047] For example, during the cruise phase, the SVM algorithm identifies a sudden increase in wind speed. Based on the impact of this wind speed variation, the aircraft's lift and drag data are corrected using a linear interpolation algorithm to generate a revised second sequence of flight simulation tests.

[0048] In one possible implementation, the first sequence of the flight simulation test is compensated for simulation scenario variations based on the multi-stage flight simulation environment to obtain a second sequence of the flight simulation test. Step S300 further includes step S310, in which simulation test scenario information is collected based on the first sequence of the flight simulation test to obtain a multi-stage simulation test scenario. Specifically, the scenario information in the first sequence of the flight simulation test is collected using sensors installed on the aircraft model (such as an inertial measurement unit (IMU), a pressure sensor, a temperature sensor, etc.). The collected scenario information (such as speed, acceleration, attitude angle, ambient temperature, air pressure, etc.) is recorded to generate a multi-stage simulation test scenario. For example, during the takeoff phase, the IMU sensor records the changes in speed and attitude angle during the takeoff phase, the pressure sensor records the changes in air pressure, and the temperature sensor records the ambient temperature.

[0049] Step S320 involves performing variation identification on the multi-stage simulated test scenario based on the multi-stage flight simulation environment to obtain variation identification results for each stage of the environment. Specifically, key features, such as velocity change rate, attitude angle deviation, and environmental parameter fluctuations, are extracted from the collected multi-stage simulated test scenario. Machine learning algorithms (such as support vector machines (SVMs) or neural networks) are used to analyze the extracted features and identify variations that are inconsistent with the expected environment (multi-stage flight simulation environment) or normal flight conditions. The identified variations are recorded to generate variation identification results for each stage of the environment. The calculation results are shown in Table 1.

[0050] Table 1: Examples of environmental variation identification results at each stage

[0051] stage Type of environmental variation Degree of variation Original data (predicted value / actual value) unit Calculation formula take off Wind speed changes +10% 5 / 5.5 m / s (5.5-5) / 5×100% Climb Air pressure fluctuations -5% 1013 / 962.35 hPa (962.35-1013) / 1013×100% cruise Temperature changes -3°C -50 / -53 °C -53-(-50) Reentry Wind speed changes +8% 100 / 108 m / s (108-100) / 100×100% landing Humidity changes +10% 60 / 66 % (66-60) / 60×100% .

[0052] Step S330 is to perform an impact analysis on the first sequence of the flight simulation test based on the environmental variation identification results of each stage to obtain the analysis results of the variation impact at each stage. Specifically, a causal analysis algorithm (such as a Bayesian network) is used to analyze the environmental variation identification results at each stage to analyze the impact of the variation on aircraft performance. The degree of influence of the variation on parameters such as aircraft speed, attitude, and fuel consumption is quantified to generate the analysis results of the variation impact at each stage. For example, during the takeoff phase, Bayesian network analysis revealed that an increase in wind speed resulted in a 10% increase in thrust demand during the takeoff phase and a 5-degree increase in attitude angle deviation. During the cruise phase, a causal analysis revealed that a decrease in air pressure resulted in a 5% increase in fuel consumption during the cruise phase and a 100-meter decrease in flight altitude.

[0053] Step S340: Based on the analysis results of the influence of the variations in each stage, the first sequence of the flight simulation test is corrected to generate the second sequence of the flight simulation test. Specifically, based on the analysis results of the influence of the variations in each stage, a data correction algorithm (such as linear interpolation or spline interpolation) is used to correct the relevant data in the first sequence of the flight simulation test. The corrected data generates the second sequence of the flight simulation test for subsequent risk prediction and optimization. For example, during the takeoff phase, the linear interpolation algorithm is used to correct the speed and attitude angle data of the takeoff phase based on the influence of wind speed variation. During the cruise phase, the spline interpolation algorithm is used to correct the fuel consumption and flight altitude data of the cruise phase based on the influence of air pressure variation.

[0054] This implementation method can accurately identify environmental variations in simulated test scenarios and quantify their impact on aircraft performance through variation recognition and impact analysis, helping to more realistically reflect the performance of aircraft in complex environments and improve the accuracy of simulation tests.

[0055] Step S400 , based on the flight test risk prediction factors, multi-dimensional risk prediction is performed on the flight test plan according to the second sequence of the flight simulation test to obtain a multi-stage test risk prediction result.

[0056] Specifically, the data from the second sequence of flight simulation tests is divided into data blocks for multiple stages, such as takeoff simulation data blocks and climb simulation data blocks. Flight test risk prediction factors are set—indicators used to assess the risks that the aircraft may encounter at different stages, such as power instability risk, structural safety risk, and attitude deviation risk. Risk prediction models (such as Markov models and deep learning models) are used to predict the risks of each stage data block, generating multi-stage test risk prediction results, including takeoff test risk prediction results and climb test risk prediction results. For example, a deep learning model was used to analyze the takeoff simulation data block, predicting the power instability risk during takeoff to be 0.05, the structural safety risk to be 0.03, and the attitude deviation risk to be 0.02.

[0057] In one possible implementation, based on the flight test risk prediction factors, a multi-dimensional risk prediction is performed on the flight test scenario according to the second sequence of the flight simulation test to obtain a multi-stage test risk prediction result. Step S400 further includes step S410: constructing a multi-stage simulation data block based on the second sequence of the flight simulation test. The multi-stage simulation data block includes a takeoff simulation data block, a climb simulation data block, a cruise simulation data block, a reentry simulation data block, and a landing simulation data block. Specifically, according to the second sequence of the flight simulation test, the data is divided into data blocks for multiple stages, including takeoff, climb, cruise, reentry, and landing. The data for each stage is organized and key parameters such as speed, acceleration, attitude angle, thrust, and fuel consumption are extracted. Simulation data blocks for each stage are generated for risk prediction analysis. For example, for the takeoff simulation data block, data such as speed, thrust, and attitude angle are extracted for the takeoff stage. For the cruise simulation data block, data such as altitude, speed, and fuel consumption are extracted for the cruise stage.

[0058] In step S420, the flight test risk prediction factors include a power instability risk index, a structural safety risk index, and an attitude deviation risk index. Specifically, the flight test risk prediction factors are defined, including a power instability risk index, a structural safety risk index, and an attitude deviation risk index. Specific quantitative standards and calculation methods are set for each risk factor, and a reasonable risk threshold is set for each risk factor to judge the risk level. For example, the power instability risk index is quantified by the ratio of thrust to drag, and the threshold is set to 1.2. The structural safety risk index is quantified by the ratio of stress to material strength, and the threshold is set to 0.8. The attitude deviation risk index is quantified by the absolute value of the attitude angle deviation, and the threshold is set to 5 degrees.

[0059] Step S430: Based on the flight test risk prediction factors, a multi-stage risk prediction is performed on the flight test plan according to the multi-stage simulation data blocks to generate the multi-stage test risk prediction results. The multi-stage test risk prediction results include a takeoff test risk prediction result, a climb test risk prediction result, a cruise test risk prediction result, a re-entry test risk prediction result, and a landing test risk prediction result. Specifically, a multi-stage risk prediction model is constructed based on the flight test risk prediction factors. A statistical model (such as regression analysis), a machine learning model (such as a random forest, a neural network), or a physical model (such as an aerodynamic model) can be used. The multi-stage simulation data blocks are input into the risk prediction model to calculate the risk index for each stage. Based on the calculation results, a multi-stage test risk prediction result is generated, including risk prediction results for the takeoff, climb, cruise, re-entry, and landing stages.

[0060] For example, for the takeoff phase, the takeoff simulation data block (speed, thrust, attitude angle, etc.) is input, the power instability risk index is calculated to be 1.1 (lower than the threshold of 1.2), the structural safety risk index is 0.7 (lower than the threshold of 0.8), and the attitude deviation risk index is 3 degrees (lower than the threshold of 5 degrees), generating a takeoff test risk prediction result: low risk.

[0061] For example, for the cruise phase, the cruise simulation data block (altitude, speed, fuel consumption, etc.) is input, and the calculated dynamic instability risk index is 1.3 (above the threshold of 1.2), the structural safety risk index is 0.9 (close to the threshold of 0.8), and the attitude deviation risk index is 4 degrees (below the threshold of 5 degrees). The resulting cruise test risk prediction result is: Medium Risk. An example of the complete multi-phase test risk prediction results is shown in Table 2.

[0062] Table 2: Example of risk prediction results for multi-stage testing

[0063] stage Dynamic instability risk index Structural safety risk indicators Posture deviation risk indicator Risk Level take off 1.1 0.7 3 degrees Low risk Climb 1.2 0.8 4 degrees Medium risk cruise 1.3 0.9 4 degrees Medium risk Reentry 1.4 1.0 5 degrees High risk landing 1.1 0.7 3 degrees Low risk .

[0064] This approach builds multi-stage simulation data blocks and sets different risk prediction factors for each stage, allowing for a comprehensive assessment of the aircraft's risk profile at different flight stages. This phased risk assessment approach is more comprehensive and detailed than a single-stage assessment.

[0065] In one possible implementation, based on the flight test risk prediction factor, a multi-stage risk prediction is performed on the flight test scenario according to the multi-stage simulation data block. Step S430 further includes step S431: identifying thrust characteristics based on the takeoff simulation data block to obtain a takeoff thrust characteristic sequence. Specifically, thrust-related data, such as engine thrust, fuel flow rate, and thrust time series, are extracted from the takeoff simulation data block. Signal processing techniques (e.g., filtering and smoothing) and statistical methods (e.g., mean and standard deviation) are used to extract thrust characteristics and generate a takeoff thrust characteristic sequence. For example, engine thrust data for the takeoff phase is extracted, and the thrust value per second is calculated to generate a thrust time series. The thrust time series is then smoothed to remove noise, resulting in a stable thrust characteristic sequence.

[0066] Step S432: Speed features are identified for the takeoff simulation data block to obtain a takeoff speed feature sequence. Specifically, speed-related data, such as ground speed, airspeed, and speed time series, are extracted from the takeoff simulation data block. Signal processing techniques (such as filtering and smoothing) and statistical methods (such as mean and standard deviation) are used to extract speed features and generate a takeoff speed feature sequence. For example, ground speed and airspeed data during takeoff are extracted, and the speed value per second is calculated to generate a speed time series. The speed time series is then smoothed to remove noise, resulting in a stable speed feature sequence.

[0067] Step S433 aligns the takeoff thrust characteristic sequence with the takeoff speed characteristic sequence to obtain a takeoff thrust-speed characteristic sequence. Specifically, the thrust characteristic sequence and the speed characteristic sequence are aligned on the time axis to ensure that their time points are consistent. The aligned thrust and speed characteristics are combined into a two-dimensional characteristic sequence, namely the takeoff thrust-speed characteristic sequence. For example, one thrust value and one speed value are recorded every second to form a two-dimensional data point sequence.

[0068] Step S434 retrieves normal takeoff thrust-speed samples based on the takeoff test plan and constructs a takeoff thrust-speed reference sequence. Specifically, based on the takeoff test plan, normal takeoff thrust-speed samples are retrieved from historical data or design specifications. These retrieved normal samples are combined into a takeoff thrust-speed reference sequence for use in risk detection. For example, normal takeoff thrust-speed samples are retrieved from historical flight data, recorded under standard atmospheric conditions. These normal samples are combined into a reference sequence, for example, with thrust values of [100, 105, 110...] and speed values of [10, 20, 30...].

[0069] Step S435: Perform a power instability risk check on the takeoff thrust-speed characteristic sequence based on the takeoff thrust-speed reference sequence to obtain a takeoff power instability risk prediction result. This takeoff power instability risk prediction result is then added to the takeoff test risk prediction result. Specifically, a power instability risk detection model is constructed, which can utilize a statistical model (e.g., regression analysis), a machine learning model (e.g., random forest, neural network), or a physical model (e.g., an aerodynamic model). The takeoff thrust-speed characteristic sequence is compared with the takeoff thrust-speed reference sequence to detect any anomalies. A power instability risk index is calculated. For example, if the deviation between thrust and speed exceeds a certain threshold, a power instability risk is determined. The power instability risk prediction result is recorded and added to the takeoff test risk prediction result. For example, a neural network model is used to analyze the takeoff thrust-speed characteristic sequence to detect any anomalies. If the thrust value is less than 10% of the reference sequence and the speed value is less than 5% of the reference sequence, a power instability risk is determined. The power instability risk prediction result is recorded and added to the takeoff test risk prediction result.

[0070] This approach extracts and aligns thrust and speed characteristics, then compares them with benchmark sequences to accurately detect the risk of dynamic instability during takeoff. This approach is more comprehensive and accurate than single-parameter risk detection.

[0071] In one possible implementation, based on the flight test risk prediction factor, a multi-stage risk prediction is performed on the flight test plan according to the multi-stage simulation data block. Step S430 further includes step S436, which reads takeoff process pressure simulation data and takeoff attitude simulation data according to the takeoff simulation data block. Specifically, pressure simulation data (such as air pressure and structural stress) and attitude simulation data (such as pitch angle, roll angle, and yaw angle) during the takeoff process are read from the takeoff simulation data block. The extracted data is preprocessed to remove noise and outliers to ensure data accuracy and usability. For example, air pressure data for the takeoff phase is extracted, ranging from 1010 hPa to 1015 hPa, and pitch angle data for the takeoff phase is extracted, ranging from 0 degrees to 10 degrees.

[0072] Step S437, perform multi-structural safety risk prediction on the aerospace vehicle based on the pressure simulation data of the takeoff process, and obtain a takeoff structural safety risk prediction result. Specifically, a structural safety risk prediction model is constructed based on the pressure simulation data of the takeoff process. A finite element analysis (FEA) model, a statistical model, or a machine learning model can be used. The model is used to calculate structural safety risk indicators, such as the ratio of structural stress to material strength. Based on the calculation results, the structural safety risk level is evaluated. For example, a finite element analysis model is used to calculate the structural stress distribution during the takeoff phase, and the material strength threshold is set to 0.8. The calculated maximum stress to material strength ratio is 0.75, which is lower than the threshold and is judged to be low risk.

[0073] Step S438 performs normal attitude fitting according to the takeoff phase test plan to determine takeoff reference attitude data. Specifically, based on the takeoff phase test plan, normal attitude data is retrieved from historical data or design specifications, and attitude fitting is performed to generate takeoff reference attitude data for use in attitude deviation risk prediction. For example, normal pitch angle data for takeoff phase is retrieved from historical flight data, ranging from 0 degrees to 8 degrees, and polynomial fitting is used to generate the takeoff reference attitude data.

[0074] In step S439, the simulated takeoff attitude data and the reference takeoff attitude data are input into an attitude deviation risk prediction model to obtain a takeoff attitude deviation risk prediction result. Specifically, the simulated takeoff attitude data and the reference takeoff attitude data are input into the attitude deviation risk prediction model to calculate attitude deviations, such as pitch angle deviation and roll angle deviation. The attitude deviation risk level is then assessed based on the magnitude of the deviations. For example, if the simulated takeoff attitude data (with a pitch angle of 10 degrees) and the reference takeoff attitude data (with a pitch angle of 8 degrees) are input, and the pitch angle deviation is calculated to be 2 degrees, the attitude deviation threshold is set to 3 degrees, resulting in a low risk assessment.

[0075] Step S440 adds the takeoff structural safety risk prediction result and the takeoff attitude deviation risk prediction result to the takeoff test risk prediction result. Specifically, the takeoff structural safety risk prediction result and the takeoff attitude deviation risk prediction result are integrated into the takeoff test risk prediction result. A comprehensive evaluation is performed on the integrated risk prediction results to generate a final takeoff test risk prediction report. For example, the structural safety risk prediction result (low risk) and the attitude deviation risk prediction result (low risk) are integrated to generate a takeoff test risk prediction report with an overall risk level of low risk.

[0076] This approach combines the predictions of structural safety risk and attitude deviation risk to provide a more comprehensive assessment of the risk during takeoff. This approach is more comprehensive and accurate than the assessment of a single risk factor.

[0077] Step S500: Sensitivity association is performed on the multi-stage flight simulation environment according to the multi-stage test risk prediction result to obtain multi-order risk environment sensitivity factors, and the flight test plan is adaptively optimized according to the multi-order risk environment sensitivity factors.

[0078] Specifically, correlation analysis algorithms (such as the Pearson correlation coefficient and the Spearman rank correlation coefficient) are used to correlate the multi-stage flight simulation environment with the multi-stage test risk prediction results to generate multi-stage risk-correlated environmental factors. Perturbation analysis algorithms (such as sensitivity analysis) are used to evaluate the perturbation impact of multi-stage risk-correlated environmental factors and generate perturbation evaluation results for each environmental factor. Optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms) are used to perform optimal analysis on the perturbation evaluation results for each environmental factor to generate multi-order risk environmental sensitivity factors, i.e., the environmental factors that have the greatest impact on aircraft risk, such as wind speed, temperature, and air pressure. Based on these multi-order risk environmental sensitivity factors, adaptive optimization algorithms (such as dynamic programming and feedback control) are used to adaptively optimize the flight test plan. Specifically, the flight test plan is dynamically adjusted based on the risk environmental sensitivity factors to improve aircraft safety and reliability.

[0079] For example, a Pearson correlation coefficient analysis revealed a correlation of 0.85 between takeoff wind speed and the risk of power instability. Based on this wind speed sensitivity factor, the takeoff speed in the flight test plan was adjusted from 200 km / h to 220 km / h to reduce the risk of power instability.

[0080] In one possible implementation, sensitivity correlation is performed on the multi-stage flight simulation environment based on the multi-stage test risk prediction results to obtain multi-stage risk environment sensitivity factors. Step S500 further includes step S510, whereby element correlation is performed on the multi-stage test risk prediction results based on the multi-stage flight simulation environment to obtain multi-stage risk-correlated environmental elements. Specifically, key environmental elements for each stage, such as temperature, pressure, wind speed, and humidity, are extracted from the multi-stage flight simulation environment. Statistical analysis methods (such as correlation analysis and regression analysis) or machine learning methods (such as association rule mining) are used to perform correlation analysis between the multi-stage test risk prediction results and environmental elements to generate multi-stage risk-correlated environmental elements, identifying which environmental elements are significantly correlated with the risk prediction results. For example, environmental elements such as temperature, pressure, and wind speed during takeoff are extracted. Using Pearson correlation analysis, it is found that wind speed during takeoff has a significant positive correlation with the risk of dynamic instability (with a correlation coefficient of 0.85). A list of risk-correlated environmental elements for the takeoff stage, including wind speed and temperature, is generated.

[0081] Step S520, perform disturbance impact evaluation on the multi-stage test risk prediction results according to the multi-stage risk-associated environmental factors, and obtain disturbance evaluation results of each environmental factor. Specifically, perform disturbance simulation on the multi-stage risk-associated environmental factors, for example, increase or decrease the values of environmental factors such as temperature and wind speed. Utilize the existing risk prediction model to evaluate the impact of disturbance on the risk prediction results, generate disturbance evaluation results for each environmental factor, and clarify which environmental factors have the greatest impact on the risk. For example, perform disturbance simulation on the wind speed during the takeoff phase, increase the wind speed from 5m / s to 7m / s, use the risk prediction model to evaluate the risk of dynamic instability after the disturbance, and find that the risk index increases from 0.1 to 0.3. Record the impact of wind speed disturbance on the risk of dynamic instability and generate disturbance evaluation results.

[0082] Step S530: Based on the disturbance evaluation results of each environmental factor, the multi-stage risk-related environmental factors are optimized and analyzed to generate the multi-order risk environment sensitivity factors. Specifically, based on the disturbance evaluation results, sensitivity analysis methods (such as sensitivity analysis and variance analysis) are used to analyze the multi-stage risk-related environmental factors to determine which factors are most sensitive to the risk. Multi-order risk environment sensitivity factors are generated. These factors are key environmental factors that affect the risk prediction results. Based on the sensitive factors, suggestions are made to optimize the flight test plan, such as adjusting the test environment parameters or increasing the number of tests. For example, using sensitivity analysis, it is found that the disturbance of wind speed during the takeoff phase has the greatest impact on the risk of dynamic instability, with a sensitivity of 0.8. Multi-order risk environment sensitivity factors for the takeoff phase are generated, including wind speed, temperature, etc., and an optimization suggestion is made: increase the number of tests under conditions of higher wind speeds to evaluate the aircraft's wind resistance.

[0083] This implementation method, through factor association, disturbance impact assessment, and optimization analysis, can accurately identify the environmental factors that have the greatest impact on risks, namely multi-order risk environmental sensitivity factors, which helps to more accurately understand the sources and impact mechanisms of risks. Based on the sensitive factors, the flight test plan can be optimized in a targeted manner. For example, if wind speed is a sensitive factor, more tests can be conducted under different wind speed conditions to evaluate the aircraft's wind resistance. This optimization can improve the efficiency and reliability of the test. By identifying sensitive factors and optimizing the test plan, potential risk points can be discovered in advance, and appropriate measures (such as adjusting test conditions and increasing safety inspections) can be taken to reduce risks and improve the safety of flight tests. The identification of sensitive factors can help the aircraft design team better understand the performance of the aircraft under different environmental conditions, thereby optimizing the aircraft design and making it more adaptable.

[0084] The embodiment of the present application adopts technical means such as constructing a multi-stage flight simulation environment with an atmospheric data system tester according to a flight test plan, conducting tests in the simulation environment, generating a first sequence of flight simulation tests, compensating the first test sequence for environmental variations, generating a second sequence, conducting multi-dimensional risk prediction based on the second sequence, generating risk prediction results, analyzing the risk prediction results, identifying sensitive factors, and optimizing the flight test plan. This solves the technical problems of incomplete and inaccurate test results caused by the limitations of environmental simulation scenarios and the lack of consideration of uncertainty factors in the adaptability tests of existing aerospace vehicles in different atmospheric environments, and achieving the technical effect of truly and comprehensively simulating the flight status of aerospace vehicles in complex atmospheric environments and improving the comprehensiveness and accuracy of test results.

[0085] In the above, refer to Figure 1 The atmospheric environment simulation method for aerospace flight simulation according to an embodiment of the present invention is described in detail. Figure 2 An atmospheric environment simulation device for aerospace flight simulation according to an embodiment of the present invention is described.

[0086] According to an embodiment of the present invention, an atmospheric environment simulation device for aerospace flight simulation is used to address the technical problem that the environmental simulation scenarios in existing aerospace aircraft adaptability tests under different atmospheric environments are limited and lack uncertainty considerations, resulting in incomplete and inaccurate test results. This device achieves the technical effect of realistically and comprehensively simulating the flight state of an aerospace aircraft in a complex atmospheric environment and improving the comprehensiveness and accuracy of the test results. The atmospheric environment simulation device for aerospace flight simulation includes: a multi-stage environment construction module 10, a simulation test module 20, a simulation scenario variation compensation module 30, a multi-dimensional risk prediction module 40, and a sensitivity association module 50.

[0087] A multi-stage environment construction module 10 is used to construct a multi-stage environment for the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment; a simulation test module 20 is used to perform a simulation test on the aerospace vehicle based on the flight test plan and the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; a simulation scenario variation compensation module 30 is used to perform simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests; a multi-dimensional risk prediction module 40 is used to perform multi-dimensional risk prediction on the flight test plan based on the flight test risk prediction factor and the second sequence of flight simulation tests to obtain a multi-stage test risk prediction result; a sensitivity association module 50 is used to perform sensitivity association on the multi-stage flight simulation environment according to the multi-stage test risk prediction result to obtain a multi-order risk environment sensitivity factor, and adaptively optimize the flight test plan according to the multi-order risk environment sensitivity factor.

[0088] The specific configuration of the simulation test module 20 will be described in detail below. As described above, based on the flight test plan, the aerospace vehicle is simulated and tested according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests. The simulation test module 20 may further include: a modeling unit for modeling the aerospace vehicle to obtain an aircraft model; a multi-stage test importance evaluation unit for performing multi-stage test importance evaluation according to the flight test plan to obtain the importance of each stage test, and constructing a multi-stage simulation test confidence mechanism based on the importance of each stage test; a multi-stage test unit for performing multi-stage test on the aircraft model according to the flight test plan and the multi-stage flight simulation environment based on the multi-stage simulation test confidence mechanism to obtain a multi-stage test data set; and a confidence fusion unit for performing confidence fusion based on the multi-stage test data set to generate the first sequence of flight simulation tests.

[0089] The specific configuration of the simulation scenario variation compensation module 30 will be described in detail below. As described above, the simulation scenario variation compensation module 30 performs simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests. The simulation scenario variation compensation module 30 may further include: a simulation test scenario information acquisition unit for acquiring simulation test scenario information based on the first sequence of flight simulation tests to obtain a multi-stage simulation test scenario; a variation identification unit for identifying variations in the multi-stage simulation test scenario according to the multi-stage flight simulation environment to obtain variation identification results for each stage environment; an impact analysis unit for performing impact analysis on the first sequence of flight simulation tests according to the variation identification results for each stage environment to obtain variation impact analysis results for each stage; and a correction unit for correcting the first sequence of flight simulation tests according to the variation impact analysis results for each stage to generate the second sequence of flight simulation tests.

[0090] Next, the specific configuration of the multi-dimensional risk prediction module 40 will be described in detail. As described above, based on the flight test risk prediction factor, the flight test plan is subjected to multi-dimensional risk prediction according to the second sequence of the flight simulation test to obtain a multi-stage test risk prediction result. The multi-dimensional risk prediction module 40 may further include: a multi-stage simulation data block construction unit for constructing a multi-stage simulation data block according to the second sequence of the flight simulation test, wherein the multi-stage simulation data block includes a takeoff simulation data block, a climb simulation data block, a cruise simulation data block, a re-entry simulation data block and a landing simulation data block; a flight test risk prediction factor setting unit for setting a flight test risk prediction factor, wherein the flight test risk prediction factor includes a power instability risk index, a structural safety risk index and an attitude deviation risk index; a multi-stage risk prediction unit for performing multi-stage risk prediction on the flight test plan according to the multi-stage simulation data block based on the flight test risk prediction factor to generate the multi-stage test risk prediction result, wherein the multi-stage test risk prediction result includes a takeoff test risk prediction result, a climb test risk prediction result, a cruise test risk prediction result, a re-entry test risk prediction result and a landing test risk prediction result.

[0091] Wherein, based on the flight test risk prediction factor, a multi-stage risk prediction is performed on the flight test plan according to the multi-stage simulation data block, and the multi-stage risk prediction unit may further include: a thrust feature identification subunit for performing thrust feature identification according to the takeoff simulation data block to obtain a takeoff thrust feature sequence; a speed feature identification subunit for performing speed feature identification on the takeoff simulation data block to obtain a takeoff speed feature sequence; an alignment subunit for aligning the takeoff thrust feature sequence and the takeoff speed feature sequence to obtain a takeoff thrust-speed feature sequence; a normal sample retrieval subunit for performing takeoff thrust-speed normal sample retrieval according to the takeoff phase test plan to construct a takeoff thrust-speed reference sequence; a power instability risk detection subunit for performing power instability risk detection on the takeoff thrust-speed feature sequence according to the takeoff thrust-speed reference sequence to obtain a takeoff power instability risk prediction result, and adding the takeoff power instability risk prediction result to the takeoff test risk prediction result.

[0092] Among them, based on the flight test risk prediction factor, the flight test plan is subjected to multi-stage risk prediction according to the multi-stage simulation data block, and the multi-stage risk prediction unit may further include: a simulation data reading subunit for reading the takeoff process pressure simulation data and the takeoff attitude simulation data according to the takeoff simulation data block; a multi-structure safety risk prediction subunit for performing multi-structure safety risk prediction on the aerospace vehicle according to the takeoff process pressure simulation data, and obtaining a takeoff structure safety risk prediction result; a normal attitude fitting subunit for performing normal attitude fitting according to the takeoff stage test plan, and determining the takeoff reference attitude data; an attitude deviation risk prediction subunit for inputting the takeoff attitude simulation data and the takeoff reference attitude data into an attitude deviation risk prediction model, and obtaining a takeoff attitude deviation risk prediction result; and a result adding subunit for adding the takeoff structure safety risk prediction result and the takeoff attitude deviation risk prediction result to the takeoff test risk prediction result.

[0093] The specific configuration of the sensitivity association module 50 will be described in detail below. As described above, the sensitivity association module 50 may further include: an element association unit for performing element association on the multi-stage test risk prediction results based on the multi-stage flight simulation environment to obtain multi-stage risk-associated environmental elements; a disturbance impact assessment unit for performing disturbance impact assessment on the multi-stage test risk prediction results based on the multi-stage risk-associated environmental elements to obtain disturbance assessment results for each environmental element; and an optimization analysis unit for performing optimization analysis on the multi-stage risk-associated environmental elements based on the disturbance assessment results for each environmental element to generate the multi-stage risk-associated environmental sensitivity factors.

[0094] The specific configuration of the multi-stage environment construction module 10 will be described in detail below. As described above, a multi-stage environment is constructed based on the flight test plan of the aerospace vehicle using the air data system tester to obtain a multi-stage flight simulation environment. The multi-stage environment construction module 10 may further include: a flight stage disassembly unit for disassembling the flight stages according to the flight test plan to obtain a multi-stage flight test plan, wherein the multi-stage flight test plan includes a takeoff phase test plan, a climb phase test plan, a cruise phase test plan, a reentry phase test plan, and a landing phase test plan; an environment prediction unit for performing environment prediction based on the multi-stage flight test plan to obtain a multi-stage flight prediction environment; and a multi-stage flight simulation environment construction unit for constructing the multi-stage flight simulation environment based on the multi-stage flight prediction environment and using the air data system tester.

[0095] Among them, based on the multi-stage flight prediction environment and according to the atmospheric data system tester, the multi-stage flight simulation environment is constructed, and the multi-stage flight simulation environment construction unit may further include: a multi-stage flight construction environment acquisition subunit for obtaining the multi-stage flight construction environment based on the multi-stage flight prediction environment and according to the atmospheric data system tester; a dynamic comparison subunit for dynamically comparing the multi-stage flight prediction environment and the multi-stage flight construction environment to obtain a multi-stage environment comparison result, and correcting the multi-stage flight construction environment according to the multi-stage environment comparison result to obtain the multi-stage flight simulation environment.

[0096] An atmospheric environment simulation device for aerospace flight simulation provided by an embodiment of the present invention can execute an atmospheric environment simulation method for aerospace flight simulation provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0097] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0098] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for simulating an atmospheric environment for aerospace flight simulation, characterized in that: The method comprises: A multi-stage environment is constructed for the flight test plan of the aerospace vehicle based on the atmospheric data system tester to obtain a multi-stage flight simulation environment; Based on the flight test plan, performing a simulation test on the aerospace vehicle in the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; performing simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests; Based on the flight test risk prediction factors, performing multi-dimensional risk prediction on the flight test plan according to the second sequence of the flight simulation test to obtain a multi-stage test risk prediction result; The multi-stage flight simulation environment is subjected to sensitivity association according to the multi-stage test risk prediction results to obtain multi-order risk environment sensitivity factors, and the flight test plan is adaptively optimized according to the multi-order risk environment sensitivity factors.

2. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that: Based on the flight test plan, the aerospace vehicle is simulated and tested according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests, including: Modeling the aerospace vehicle to obtain a vehicle model; Conducting a multi-stage test importance evaluation according to the flight test plan to obtain the test importance of each stage, and constructing a multi-stage simulation test confidence mechanism based on the test importance of each stage; Based on the multi-stage simulation test confidence mechanism, perform a multi-stage test on the aircraft model according to the flight test plan and the multi-stage flight simulation environment to obtain a multi-stage test data set; Confidence fusion is performed based on the multi-stage test data set to generate the first sequence of the flight simulation test.

3. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that: Performing simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests, including: Collecting simulation test scenario information according to the first sequence of the flight simulation test to obtain a multi-stage simulation test scenario; performing variation identification on the multi-stage simulation test scenario according to the multi-stage flight simulation environment to obtain variation identification results of the environment in each stage; Performing impact analysis on the first sequence of the flight simulation test according to the environmental variation identification results of each stage to obtain the variation impact analysis results of each stage; The first sequence of flight simulation tests is modified according to the analysis results of the variation impacts of each stage to generate the second sequence of flight simulation tests.

4. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, wherein: Based on the flight test risk prediction factors, a multi-dimensional risk prediction is performed on the flight test plan according to the second sequence of the flight simulation test to obtain a multi-stage test risk prediction result, including: constructing a multi-stage simulation data block according to the second sequence of the flight simulation test, wherein the multi-stage simulation data block includes a takeoff simulation data block, a climb simulation data block, a cruise simulation data block, a reentry simulation data block, and a landing simulation data block; The flight test risk prediction factors include a power instability risk index, a structural safety risk index, and an attitude deviation risk index; Based on the flight test risk prediction factor, a multi-stage risk prediction is performed on the flight test plan according to the multi-stage simulation data block to generate the multi-stage test risk prediction result, which includes a takeoff test risk prediction result, a climb test risk prediction result, a cruise test risk prediction result, a re-entry test risk prediction result and a landing test risk prediction result.

5. The atmospheric environment simulation method for aerospace flight simulation according to claim 4, characterized in that: Based on the flight test risk prediction factor, performing a multi-stage risk prediction on the flight test plan according to the multi-stage simulation data block, including: performing thrust feature recognition according to the takeoff simulation data block to obtain a takeoff thrust feature sequence; performing speed feature recognition on the takeoff simulation data block to obtain a takeoff speed feature sequence; aligning the takeoff thrust characteristic sequence and the takeoff speed characteristic sequence to obtain a takeoff thrust-speed characteristic sequence; According to the takeoff phase test plan, a normal takeoff thrust-speed sample is retrieved to construct a takeoff thrust-speed reference sequence; A power instability risk detection is performed on the takeoff thrust-speed characteristic sequence according to the takeoff thrust-speed reference sequence to obtain a takeoff power instability risk prediction result, and the takeoff power instability risk prediction result is added to the takeoff test risk prediction result.

6. The atmospheric environment simulation method for aerospace flight simulation according to claim 4, characterized in that: Based on the flight test risk prediction factor, performing a multi-stage risk prediction on the flight test plan according to the multi-stage simulation data block, including: Reading takeoff process pressure simulation data and takeoff attitude simulation data according to the takeoff simulation data block; Performing multi-structure safety risk prediction on the aerospace vehicle according to the pressure simulation data of the takeoff process to obtain a takeoff structure safety risk prediction result; Perform normal attitude fitting according to the takeoff phase test plan to determine the takeoff reference attitude data; Inputting the takeoff attitude simulation data and the takeoff reference attitude data into an attitude deviation risk prediction model to obtain a takeoff attitude deviation risk prediction result; The takeoff structure safety risk prediction result and the takeoff attitude deviation risk prediction result are added to the takeoff test risk prediction result.

7. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that: Performing sensitivity correlation on the multi-stage flight simulation environment according to the multi-stage test risk prediction results to obtain a multi-stage risk environment sensitivity factor, including: Correlating the multi-stage test risk prediction results with elements according to the multi-stage flight simulation environment to obtain multi-stage risk correlation environment elements; Performing a disturbance impact evaluation on the multi-stage test risk prediction results according to the multi-stage risk-associated environmental factors to obtain a disturbance evaluation result of each environmental factor; The multi-stage risk-related environmental factors are optimized and analyzed according to the disturbance evaluation results of each environmental factor to generate the multi-stage risk environmental sensitivity factors.

8. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that: A multi-stage environment is constructed for the flight test plan of the aerospace vehicle based on the atmospheric data system tester to obtain a multi-stage flight simulation environment, including: Disassembling the flight phases according to the flight test plan to obtain a multi-phase flight test plan, wherein the multi-phase flight test plan includes a takeoff phase test plan, a climb phase test plan, a cruise phase test plan, a reentry phase test plan, and a landing phase test plan; Performing environmental prediction according to the multi-stage flight test plan to obtain a multi-stage flight prediction environment; Based on the multi-stage flight prediction environment and according to the atmospheric data system tester, the multi-stage flight simulation environment is constructed.

9. The atmospheric environment simulation method for aerospace flight simulation according to claim 8, characterized in that: Based on the multi-stage flight prediction environment and according to the air data system tester, the multi-stage flight simulation environment is constructed, including: Based on the multi-stage flight prediction environment, and according to the air data system tester, a multi-stage flight construction environment is obtained; Dynamically compare the multi-stage flight prediction environment and the multi-stage flight construction environment to obtain a multi-stage environment comparison result, and correct the multi-stage flight construction environment according to the multi-stage environment comparison result to obtain the multi-stage flight simulation environment.

10. An atmospheric environment simulation device for aerospace flight simulation, characterized in that: The device is used to implement the atmospheric environment simulation method for aerospace flight simulation according to any one of claims 1 to 9, and the device comprises: A multi-stage environment construction module is used to construct a multi-stage environment for the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment; A simulation test module, configured to perform a simulation test on the aerospace vehicle based on the flight test plan and in the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; a simulation scenario variation compensation module, configured to perform simulation scenario variation compensation on the first sequence of flight simulation tests according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests; a multidimensional risk prediction module, configured to perform a multidimensional risk prediction on the flight test plan based on the flight test risk prediction factors and according to the second sequence of the flight simulation test, to obtain a multi-stage test risk prediction result; A sensitivity association module is used to perform sensitivity association on the multi-stage flight simulation environment according to the multi-stage test risk prediction results, obtain multi-order risk environment sensitivity factors, and adaptively optimize the flight test plan according to the multi-order risk environment sensitivity factors.

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