Atmospheric environment simulation method and device for aerospace flight simulation

Abstract

The invention discloses an atmospheric environment simulation method and device for aerospace flight simulation, and relates to the related field of data processing, and the method comprises the steps: carrying out the multi-stage environment construction of a flight test scheme of an aerospace vehicle according to an atmospheric data system tester, and obtaining a multi-stage flight simulation environment; performing a simulation test on the aerospace vehicle to obtain a first test sequence; performing simulation scene variation compensation to obtain a second test sequence; performing multi-dimensional risk prediction on the test scheme to obtain a multi-stage test risk prediction result; and carrying out sensitivity association to obtain a multi-order risk environment sensitive factor, and optimizing a test scheme. The technical problem that the test result is incomplete and inaccurate due to the fact that the environment simulation scene is limited and uncertain factor consideration is lacked in the adaptability test of the aerospace vehicle in different atmospheric environments in the prior art is solved, the flight state of the aerospace vehicle in the complex atmospheric environment is truly and comprehensively simulated, and the test efficiency is improved. And the comprehensiveness and the accuracy of a test result are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular, to a method and device for simulating the atmospheric environment in aerospace flight simulation. Background Art

[0002] In the field of research and development and testing of aerospace vehicles, it is crucial to ensure the safe and reliable operation of the vehicle in a complex and changing real atmospheric environment, which 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 conventional simulation tests on the vehicle based on these preset scenarios. However, the current method has a single constructed environmental simulation scenario, lacks dynamic changes, and does not fully consider various uncertainty factors that may occur during the simulation test, resulting in the test results being difficult to comprehensively and accurately reflect the performance and potential risks of the vehicle in the actual complex atmospheric environment, and unable to provide sufficient and effective optimization basis for the flight test plan.

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

[0004] This application provides a method and device for simulating the atmospheric environment in aerospace flight simulation. By using technical means such as constructing a multi-stage flight simulation environment with an atmospheric data system tester according to the flight test plan, conducting tests in the simulation environment to generate the first sequence of flight simulation tests, performing environmental variation compensation on the first sequence of tests to generate a second sequence, conducting multi-dimensional risk prediction based on the second sequence to generate a risk prediction result, analyzing the risk prediction result to identify sensitive factors, and optimizing the flight test plan, this application solves the technical problems of limitations in environmental simulation scenarios and lack of consideration of uncertainty factors in the adaptability testing of existing aerospace vehicles in different atmospheric environments, resulting in incomplete and inaccurate test results, and achieves the technical effect of truly and comprehensively simulating the flight state of aerospace vehicles in a complex atmospheric environment and improving the comprehensiveness and accuracy of test results.

[0005] This application provides an atmospheric environment simulation method for aerospace flight simulation, including: constructing multi-stage environments for the 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 simulation tests on the aerospace vehicle according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; performing simulation scenario mutation 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 flight test risk prediction factors, performing multi-dimensional risk prediction on the flight test plan according to the second sequence of flight simulation tests to obtain multi-stage test risk prediction results; performing sensitivity correlation on the multi-stage flight simulation environment according to the multi-stage test risk prediction results to obtain multi-stage risk environment sensitivity factors, and adaptively optimizing the flight test plan according to the multi-stage risk environment sensitivity factors.

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

[0007] In a possible implementation manner, performing simulation scenario mutation 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 following processing is performed: collecting simulation test scenario information according to the first sequence of flight simulation tests to obtain a multi-stage simulation test scenario; performing mutation identification on the multi-stage simulation test scenario according to the multi-stage flight simulation environment to obtain the mutation identification results of each stage of the environment; performing impact analysis on the first sequence of flight simulation tests according to the mutation identification results of each stage of the environment to obtain the impact analysis results of each stage of the mutation; correcting the first sequence of flight simulation tests according to the impact analysis results of each stage of the mutation to generate the second sequence of flight simulation tests.

[0008] In a possible implementation manner, 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 flight simulation tests to obtain a multi-stage test risk prediction result, and the following processing is performed: according to the second sequence of flight simulation tests, 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 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 factors, 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, 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 reentry test risk prediction result, and a landing test risk prediction result.

[0009] In a possible implementation manner, 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 block, and the following processing is performed: thrust characteristic identification is performed according to the takeoff simulation data block to obtain a takeoff thrust characteristic sequence; speed characteristic identification is performed 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; a takeoff thrust-speed normal sample retrieval is performed according to the takeoff stage test plan 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.

[0010] In a possible implementation manner, 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 block, and the following processing is performed: according to the takeoff simulation data block, takeoff process pressure simulation data and takeoff attitude simulation data are read; a multi-structural safety risk prediction is performed on the aerospace vehicle according to the takeoff process pressure simulation data to obtain a takeoff structural safety risk prediction result; a normal attitude fitting is performed according to the takeoff stage test plan to determine takeoff reference attitude data; the takeoff attitude simulation data and the takeoff reference attitude data are input into an attitude deviation risk prediction model to obtain a takeoff attitude deviation risk prediction result; the takeoff structural 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 manner, sensitivity association is performed on the multi-stage flight simulation environment according to the multi-stage test risk prediction result to obtain a multi-stage risk environment sensitivity factor, and the following processing is performed: Element association is performed on the multi-stage test risk prediction result according to the multi-stage flight simulation environment to obtain multi-stage risk-associated environmental elements; a perturbation impact evaluation is performed on the multi-stage test risk prediction result according to the multi-stage risk-associated environmental elements to obtain a perturbation evaluation result for each environmental element; and optimization analysis is performed on the multi-stage risk-associated environmental elements according to the perturbation evaluation result for each environmental element to generate the multi-stage risk environment sensitivity factor.

[0012] In a possible implementation manner, 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 test plan is disassembled according to the flight test plan to obtain a multi-stage flight test plan, and the multi-stage flight test plan includes a takeoff stage test plan, a climb stage test plan, a cruise stage test plan, a reentry stage test plan, and a landing stage test plan; Environment prediction is performed according to the multi-stage flight test plan to obtain a multi-stage flight prediction environment; and 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 manner, based on the multi-stage flight prediction environment, the multi-stage flight simulation environment is constructed according to the atmospheric data system tester, and the following processing is performed: Based on the multi-stage flight prediction environment, a multi-stage flight construction environment is obtained according to the atmospheric data system tester; a dynamic comparison is performed between the multi-stage flight prediction environment and the multi-stage flight construction environment 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 for constructing a multi-stage environment according to the flight test plan of an aerospace vehicle by means of an atmospheric data system tester to obtain a multi-stage flight simulation environment; a simulation test module for performing a simulation test on the aerospace vehicle based on the flight test plan and according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; a simulation scenario variation compensation module for compensating the first sequence of flight simulation tests for simulation scenario variations according to the multi-stage flight simulation environment to obtain a second sequence of flight simulation tests; a multi-dimensional risk prediction module for performing multi-dimensional risk prediction on the flight test plan based on flight test risk prediction factors and according to the second sequence of flight simulation tests to obtain a multi-stage test risk prediction result; and a sensitivity correlation module for correlating the sensitivity of the multi-stage flight simulation environment according to the multi-stage test risk prediction result to obtain multi-stage risk environment sensitivity factors, and adaptively optimizing the flight test plan according to the multi-stage risk environment sensitivity factors.

[0015] It is intended to propose an atmospheric environment simulation method and device for aerospace flight simulation through the present application. First, a multi-stage environment is constructed according to the flight test plan of an aerospace vehicle by means of an atmospheric data system tester to obtain a multi-stage flight simulation environment. Then, based on the flight test plan, a simulation test is performed on the aerospace vehicle according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests. Next, 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. Furthermore, multi-dimensional risk prediction is performed on the flight test plan based on flight test risk prediction factors and according to the second sequence of flight simulation tests to obtain a multi-stage test risk prediction result. Finally, the sensitivity of the multi-stage flight simulation environment is correlated according to the multi-stage test risk prediction result to obtain multi-stage risk environment sensitivity factors, and the flight test plan is adaptively optimized according to the multi-stage risk environment sensitivity factors. The technical effect of truly and comprehensively simulating the flight state of an aerospace vehicle in a complex atmospheric environment and improving the comprehensiveness and accuracy of test results is achieved. Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in the present application to illustrate the operations performed by the device according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 Schematic flowchart of an atmospheric environment simulation method for aerospace flight simulation provided by an embodiment of the present application.

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

[0019] Explanation of reference numerals: multi-stage environment construction module 10, simulation test module 20, simulation scene mutation compensation module 30, multi-dimensional risk prediction module 40, sensitivity correlation module 50. Detailed implementation manners

[0020] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0022] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first\second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are 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 technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0023] An embodiment of the present application provides an atmospheric environment simulation method for aerospace flight simulation, as Figure 1 shown, the method includes: Step S100, perform multi-stage environment construction on the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment.

[0024] Specifically, according to the flight test plan, the entire flight process is disassembled into multiple stages, such as the takeoff stage, climb stage, cruise stage, reentry stage, and landing stage. The atmospheric data system tester (ADS, an instrument for measuring atmospheric parameters, installed on the aircraft or ground weather station) is used to obtain the atmospheric parameters (such as temperature, pressure, wind speed, humidity, etc.) of each stage, and combined with the meteorological model to predict the atmospheric environment of each stage. A multi-stage flight simulation environment is constructed through computer simulation software (such as MATLAB / Simulink, ANSYS Fluent, etc.), and the predicted atmospheric parameters are input into the simulation model to generate the virtual atmospheric environment of each stage.

[0025] For example, for the takeoff stage, the ADS tester measures the temperature at the takeoff airport as 25°C, the air pressure as 1013 hPa, and the wind speed as 5 m / s. These parameters are input into the simulation software to construct the atmospheric environment of the takeoff stage. For the cruise stage, the ADS tester combines meteorological satellite data to predict the atmospheric temperature at the cruise altitude (10 km) as -50°C, the air pressure as 260 hPa, and the wind speed as 100 m / s. These parameters are input into the simulation model to generate the environment of the cruise stage.

[0026] In a possible implementation, according to the flight test plan of the aerospace vehicle by the atmospheric data system tester, a multi-stage environment is constructed to obtain a multi-stage flight simulation environment. Step S100 further includes step S110 of disassembling the flight stage according to the flight test plan to obtain a multi-stage flight test plan, and the multi-stage flight test plan includes a takeoff stage test plan, a climb stage test plan, a cruise stage test plan, a reentry stage test plan, and a landing stage test plan. Specifically, a detailed analysis of the flight test plan of the aerospace vehicle is carried out to clarify each key stage in the flight process. The entire flight process is divided into multiple stages, including the takeoff stage, climb stage, cruise stage, reentry stage, and landing stage. Each stage has its 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 takeoff stage test plan tests parameters such as the engine thrust, takeoff speed, and attitude control of the aircraft, the climb stage test plan tests parameters such as the climb rate, fuel consumption, and aerodynamic performance of the aircraft, the cruise stage test plan tests parameters such as the range, fuel efficiency, and flight altitude stability of the aircraft, the reentry stage test plan tests parameters such as the thermal protection performance and aerodynamic deceleration performance when the aircraft reenters the atmosphere, and the landing stage test plan tests parameters such as the landing speed, landing attitude, and braking performance of the aircraft.

[0027] Step S120: Conduct environmental prediction according to the multi-stage flight test plan to obtain the multi-stage flight prediction environment. Specifically, use an Atmospheric Data System Tester (ADS) to obtain atmospheric parameters such as temperature, pressure, wind speed, and humidity for the current and a period of time in the future. Combine meteorological models (such as global meteorological forecast models and local meteorological models) to predict the atmospheric environment for each stage. Generate the flight prediction environment for each stage according to the prediction results. For example, at the takeoff stage, the ADS tester measures the temperature at the departure airport as 25°C, the air pressure as 1013 hPa, and the wind speed as 5 m / s, and combines the meteorological model to predict the atmospheric environment during the takeoff stage within the next 1 hour. At the cruise stage, the ADS tester combines meteorological satellite data to predict the atmospheric temperature at the cruise altitude (10 km) as -50°C, the air pressure as 260 hPa, and the wind speed as 100 m / s, and generates the prediction environment for the cruise stage.

[0028] Step S130: Based on the multi-stage flight prediction environment, construct the multi-stage flight simulation environment according to the Atmospheric Data System Tester. Specifically, input the multi-stage flight prediction environment parameters generated in step S120 into computer simulation software, and use the simulation software (such as MATLAB / Simulink, ANSYS Fluent) to construct the virtual atmospheric environment for each stage. Calibrate the constructed virtual environment to ensure it is consistent with the actual prediction environment. For example, for the takeoff stage, input the parameters such as temperature, pressure, and wind speed measured by the ADS tester into MATLAB / Simulink to construct the virtual atmospheric environment for the takeoff stage. For the cruise stage, input the predicted parameters such as temperature, pressure, and wind speed into ANSYS Fluent to generate the virtual atmospheric environment for the cruise stage.

[0029] This implementation method can more comprehensively test the performance of the aircraft in different flight stages by dividing the flight process into multiple stages and formulating detailed test plans for each stage. At the same time, by combining the ADS tester and meteorological models to predict the environment, it can more accurately simulate the actual flight environment, improving the comprehensiveness and accuracy of the test.

[0030] In a possible implementation, based on the multi-stage flight prediction environment, a multi-stage flight simulation environment is constructed according to the atmospheric data system tester. Step S130 further includes step S131 of obtaining a multi-stage flight construction environment based on the multi-stage flight prediction environment and according to the atmospheric data system tester. Specifically, according to the multi-stage flight prediction environment, the atmospheric data system tester (ADS) is used to collect current environmental parameters, including temperature, pressure, wind speed, humidity, etc. The collected environmental parameters are input into computer simulation software (such as MATLAB / Simulink, ANSYS Fluent) to preliminarily construct the flight environment for each stage and generate a multi-stage flight construction environment, including virtual environments for takeoff, climb, cruise, reentry, and landing stages.

[0031] For example, for the takeoff stage, the ADS tester measures the temperature at the departure airport as 25°C, the air pressure as 1013 hPa, and the wind speed as 5 m / s. These parameters are input into MATLAB / Simulink to preliminarily construct the virtual atmospheric environment for the takeoff stage. For the cruise stage, the ADS tester combines meteorological satellite data to predict that the atmospheric temperature at the cruise altitude (10 km) is -50°C, the air pressure is 260 hPa, and the wind speed is 100 m / s. These parameters are input into ANSYS Fluent to generate the virtual atmospheric environment for the cruise stage.

[0032] Step S132 is to 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. Specifically, the multi-stage flight prediction environment is compared with the multi-stage flight construction environment in real time, and the comparison content includes 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. According to the comparison result, the multi-stage flight construction environment is corrected, and the correction methods include parameter adjustment, model modification, etc., to ensure the consistency between the construction environment and the prediction environment. The corrected environment is the multi-stage flight simulation environment, which is used for subsequent aircraft tests.

[0033] This implementation ensures the consistency between the multi-stage flight construction environment and the prediction environment through dynamic comparison and correction, thereby improving the accuracy of the simulation environment and helping to more realistically reflect the environmental conditions that the aircraft may encounter during actual flight.

[0034] Step S200 is to conduct a simulation test on the aerospace vehicle according to the multi-stage flight simulation environment based on the flight test plan to obtain the first sequence of flight simulation tests.

[0035] Specifically, computer-aided design (CAD) software (such as SolidWorks, CATIA) and simulation software (such as MATLAB / Simulink) are used to model the aerospace vehicle to generate a digital model of the vehicle. According to the flight test plan, a weight assignment algorithm (such as the Analytic Hierarchy Process AHP) is used to evaluate the importance of each stage of the test, and the importance degree of each stage of the test is generated. According to the importance degree of each stage of the test, a multi-stage simulation test confidence mechanism is generated in combination with the Monte Carlo method to ensure the reliability of the test. In the multi-stage flight simulation environment, multi-stage tests are carried out on the vehicle model, and the test data of each stage (such as speed, acceleration, attitude angle, etc.) are recorded to generate a multi-stage test data set. A data fusion algorithm (such as Kalman filtering) is used to fuse the multi-stage test data set to generate the first sequence of flight simulation tests.

[0036] For example, SolidWorks is used to establish a three-dimensional model of the vehicle, including components such as wings, fuselage, and engines. A dynamic model of the vehicle, including an aerodynamic model, a propulsion model, etc., is established in MATLAB / Simulink. During the takeoff stage, data such as the thrust, speed, and attitude angle of the vehicle are recorded; during the cruise stage, data such as the lift, drag, and fuel consumption of the vehicle are recorded.

[0037] In a possible implementation manner, based on the flight test plan, the aerospace vehicle is simulated and tested according to the multi-stage flight simulation environment to obtain the first sequence of flight simulation tests. Step S200 further includes step S210 of obtaining a vehicle model by modeling the aerospace vehicle. Specifically, computer-aided design (CAD) software (such as SolidWorks, CATIA) is used to create a three-dimensional geometric model of the aerospace vehicle, including components such as wings, fuselage, and engines. Physical properties, such as mass distribution, aerodynamic characteristics, and propulsion systems, are added to the vehicle model in simulation software (such as MATLAB / Simulink, ANSYS Fluent). A flight control system model, including attitude control, speed control, and other modules, is added to the vehicle model.

[0038] For example, a three-dimensional model of the vehicle is created in SolidWorks, including the detailed structures of the wings, fuselage, and engines. Aerodynamic characteristic parameters, such as lift coefficient and drag coefficient, are added to the vehicle model in MATLAB / Simulink. An attitude control algorithm is added to the vehicle model to ensure that the vehicle can maintain a stable flight attitude during the simulation test.

[0039] Step S220: Conduct a multi-stage test importance evaluation according to the flight test plan, obtain the importance degree of each stage of the test, and construct a multi-stage simulation test confidence mechanism based on the importance degree of each stage of the test. Specifically, according to the flight test plan, use the Analytic Hierarchy Process (AHP) or other weight assignment algorithms to evaluate the importance of tests in each stage such as takeoff, climb, cruise, reentry, and landing, and assign weights. According to the importance degree of each stage of the test, combined with the Monte Carlo method, determine the number of simulation tests for each stage, and construct a multi-stage simulation test confidence mechanism.

[0040] For example, through AHP analysis, it is determined that the importance degree of the takeoff stage test is 0.3, the climb stage is 0.2, the cruise stage is 0.3, the reentry stage is 0.1, and the landing stage is 0.1. According to the weights, determine the number of simulation tests for each stage. For example, conduct 15 tests in the takeoff stage, 10 tests in the climb stage, 15 tests in the cruise stage, 5 tests in the reentry stage, and 5 tests in the landing stage.

[0041] Step S230: Based on the multi-stage simulation test confidence mechanism, conduct multi-stage tests on the aircraft model according to the flight test plan and the multi-stage flight simulation environment, and obtain a multi-stage test data set. Specifically, according to the multi-stage flight simulation environment, set corresponding test environment parameters for each stage. Run the aircraft model in the simulation software, conduct multi-stage tests according to the predetermined test plan and environment parameters, record the test data of each stage, including speed, acceleration, attitude angle, fuel consumption, etc. Use a data acquisition system (such as the data recording module in MATLAB / Simulink) to collect the test data and generate a multi-stage test data set. For example, collect the test data of 15 takeoff stages and generate a test data set for the takeoff stage.

[0042] Step S240: Conduct confidence fusion according to the multi-stage test data set to generate the first sequence of the flight simulation test. Specifically, preprocess the multi-stage test data set to remove outliers and noise. According to the number of tests, conduct confidence fusion on the test data of each stage. For example, take the mean value of the test data of each stage to generate the first sequence of the flight simulation test. For example, preprocess the test data of 15 takeoff stages, remove outliers, calculate the mean value of the test data of the takeoff stage, and generate the test result of the takeoff stage.

[0043] This implementation method can reasonably allocate test resources, avoid resource waste, and improve test efficiency by allocating the number of tests according to the importance degree of each stage of the test.

[0044] Step S300: Conduct simulated scenario mutation compensation on the first sequence of the flight simulation test according to the multi-stage flight simulation environment to obtain the second sequence of the flight simulation test.

[0045] Specifically, the simulation scenario variation compensation refers to correcting the environmental variations (such as wind speed changes and air pressure fluctuations) that occur in the simulation test scenario to be closer to the real flight environment. A sensor network (such as an IMU inertial measurement unit and a pressure sensor) is used to collect the scenario information in the first sequence of flight simulation tests, and a multi-stage simulation test scenario is generated. Machine learning algorithms (such as support vector machine SVM and neural network) are used to identify variations in the multi-stage simulation test scenario, and the environmental variation identification results for each stage are generated. A causal analysis algorithm (such as a Bayesian network) is used to analyze the impact of the environmental variation identification results for each stage, and the variation impact analysis results for each stage are generated. According to the variation impact analysis results for each stage, an interpolation algorithm (such as linear interpolation and spline interpolation) is used to correct the first sequence of flight simulation tests, and a second sequence of flight simulation tests is generated.

[0046] For example, during the cruise stage, the SVM algorithm is used to identify the variation of a sudden increase in wind speed. According to the impact of the wind speed variation, the lift and drag data of the aircraft are corrected using the linear interpolation algorithm, and a second sequence of corrected flight simulation tests is generated.

[0047] In a possible implementation, according to the multi-stage flight simulation environment, the first sequence of flight simulation tests is compensated for simulation scenario variations to obtain a second sequence of flight simulation tests. Step S300 further includes step S310 of collecting simulation test scenario information according to the first sequence of flight simulation tests to obtain a multi-stage simulation test scenario. Specifically, sensors installed on the aircraft model (such as an IMU inertial measurement unit, a pressure sensor, a temperature sensor, etc.) are used to collect the scenario information in the first sequence of flight simulation tests. The collected scenario information (such as speed, acceleration, attitude angle, environmental temperature, air pressure, etc.) is recorded to generate a multi-stage simulation test scenario. For example, during the takeoff stage, the IMU sensor records the speed and attitude angle changes during the takeoff stage, the pressure sensor records the air pressure change, and the temperature sensor records the environmental temperature.

[0048] Step S320 is to identify variations in the multi-stage simulation test scenario according to the multi-stage flight simulation environment to obtain the environmental variation identification results for each stage. Specifically, key features are extracted from the collected multi-stage simulation test scenario, such as the speed change rate, attitude angle deviation, and environmental parameter fluctuations. Machine learning algorithms (such as support vector machine SVM and neural network) are used to analyze the extracted features to identify variations that do not conform to the expected environment (multi-stage flight simulation environment) or normal flight state. The identified variations are recorded to generate the environmental variation identification results for each stage, and the calculation results are shown in Table 1.

[0049] Table 1: Example of environmental variation identification results for each stage Phase Environmental Variation Type Degree of Variation Original Data (Predicted Value / Actual Value) Unit Calculation Formula Takeoff Wind Speed Change +10% 5 / 5.5 m / s (5.5-5) / 5×100% Climb Air Pressure Fluctuation -5% 1013 / 962.35 hPa (962.35-1013) / 1013×100% Cruise Temperature Change -3°C -50 / -53 °C -53-(-50) Reentry Wind Speed Change +8% 100 / 108 m / s (108-100) / 100×100% Landing Humidity Change +10% 60 / 66 % (66-60) / 60×100% 。

[0050] Step S330: Analyze the impact on the first sequence of flight simulation tests based on the environmental variation identification results at each stage to obtain the impact analysis results of variation at each stage. Specifically, use a causal analysis algorithm (such as a Bayesian network) to analyze the environmental variation identification results at each stage, and analyze the impact of the variation on the performance of the aircraft. Quantify the impact degree of the variation on parameters such as the speed, attitude, and fuel consumption of the aircraft, and generate the impact analysis results of variation at each stage. For example, in the takeoff stage, through Bayesian network analysis, it is found that an increase in wind speed leads to a 10% increase in thrust demand and a 5-degree increase in attitude angle deviation during the takeoff stage. In the cruise stage, through causal analysis, it is found that a decrease in air pressure leads to a 5% increase in fuel consumption and a 100-meter decrease in flight altitude during the cruise stage.

[0051] Step S340: Modify the first sequence of flight simulation tests according to the impact analysis results of variation at each stage to generate the second sequence of flight simulation tests. Specifically, according to the impact analysis results of variation at each stage, use a data correction algorithm (such as linear interpolation or spline interpolation) to correct the relevant data in the first sequence of flight simulation tests. The corrected data generates the second sequence of flight simulation tests for subsequent risk prediction and optimization. For example, in the takeoff stage, according to the impact of wind speed variation, use the linear interpolation algorithm to correct the speed and attitude angle data in the takeoff stage. In the cruise stage, according to the impact of air pressure variation, use the spline interpolation algorithm to correct the fuel consumption and flight altitude data in the cruise stage.

[0052] This implementation method can accurately identify environmental variations in the simulation test scenario and quantify their impact on the performance of the aircraft through variation identification and impact analysis, which helps to more realistically reflect the performance of the aircraft in complex environments and improve the accuracy of the simulation test.

[0053] Step S400: Based on the flight test risk prediction factors, conduct multi-dimensional risk prediction on the flight test plan according to the second sequence of flight simulation tests to obtain the multi-stage test risk prediction results.

[0054] Specifically, the data in the second sequence of flight simulation tests is divided into data blocks for multiple stages, such as takeoff simulation data blocks, climb simulation data blocks, etc. Flight test risk prediction factors are set, that is, indicators used to evaluate the risks that the aircraft may encounter at different stages, such as power instability risk indicators, structural safety risk indicators, and attitude deviation risk indicators, etc. A risk prediction model (such as a Markov model, a deep learning model) is used to predict the risks of each stage data block, generating multi-stage test risk prediction results, including takeoff test risk prediction results, climb test risk prediction results, etc. For example, using a deep learning model to analyze the takeoff simulation data block, the predicted power instability risk in the takeoff stage is 0.05, the structural safety risk is 0.03, and the attitude deviation risk is 0.02.

[0055] In a possible implementation manner, based on the flight test risk prediction factors, according to the second sequence of the flight simulation test, a multi-dimensional risk prediction is performed on the flight test plan to obtain multi-stage test risk prediction results. Step S400 further includes step S410 of constructing a multi-stage simulation data block according to 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 stages. The data for each stage is sorted out, and key parameters are extracted, such as speed, acceleration, attitude angle, thrust, fuel consumption, etc. A simulation data block for each stage is generated for risk prediction analysis. For example, for the takeoff simulation data block, data such as speed, thrust, and attitude angle in the takeoff stage are extracted. For the cruise simulation data block, data such as altitude, speed, and fuel consumption in the cruise stage are extracted.

[0056] Step S420, the flight test risk prediction factors include a power instability risk indicator, a structural safety risk indicator, and an attitude deviation risk indicator. Specifically, flight test risk prediction factors are defined, including a power instability risk indicator, a structural safety risk indicator, and an attitude deviation risk indicator. Specific quantification criteria and calculation methods are set for each risk factor, and a reasonable risk threshold is set for each risk factor to be used to judge the risk level. For example, the power instability risk indicator is quantified by the ratio of thrust to drag, and the set threshold is 1.2. The structural safety risk indicator is quantified by the ratio of stress to material strength, and the set threshold is 0.8. The attitude deviation risk indicator is quantified by the absolute value of the attitude angle deviation, and the set threshold is 5 degrees.

[0057] Step S430: Based on the flight test risk prediction factors, perform multi-stage risk prediction on the flight test plan according to the multi-stage simulation data blocks to generate the multi-stage test risk prediction results, which include takeoff test risk prediction results, climb test risk prediction results, cruise test risk prediction results, re-entry test risk prediction results, and landing test risk prediction results. Specifically, construct a multi-stage risk prediction model according to the flight test risk prediction factors. Statistical models (such as regression analysis), machine learning models (such as random forest, neural network), or physical models (such as aerodynamic models) can be used. Input the multi-stage simulation data blocks into the risk prediction model to calculate the risk indicators for each stage. According to the calculation results, generate the multi-stage test risk prediction results, including the risk prediction results for the takeoff, climb, cruise, re-entry, and landing stages.

[0058] For example, for the takeoff stage, input the takeoff simulation data blocks (speed, thrust, attitude angle, etc.), calculate that the dynamic instability risk indicator is 1.1 (lower than the threshold of 1.2), the structural safety risk indicator is 0.7 (lower than the threshold of 0.8), and the attitude deviation risk indicator is 3 degrees (lower than the threshold of 5 degrees), and generate the takeoff test risk prediction result: low risk.

[0059] For example, for the cruise stage, input the cruise simulation data blocks (altitude, speed, fuel consumption, etc.), calculate that the dynamic instability risk indicator is 1.3 (higher than the threshold of 1.2), the structural safety risk indicator is 0.9 (close to the threshold of 0.8), and the attitude deviation risk indicator is 4 degrees (lower than the threshold of 5 degrees), and generate the cruise test risk prediction result: medium risk. An example of the complete multi-stage test risk prediction results is shown in Table 2.

[0060] Table 2: Example of multi-stage test risk prediction results Phase Power Instability Risk Index Structural Safety Risk Index Attitude Deviation Risk Index Risk Level Takeoff 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 。

[0061] In this implementation method, by constructing multi-stage simulation data blocks and setting different risk prediction factors for each stage, the risk situation of the aircraft in different flight stages can be comprehensively evaluated. This phased risk assessment method is more comprehensive and detailed than the single-stage assessment.

[0062] In a possible implementation manner, 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 S431, where thrust feature recognition is performed according to the takeoff simulation data block to obtain a takeoff thrust feature sequence. Specifically, data related to thrust, such as engine thrust, fuel flow rate, thrust time series, etc., is extracted from the takeoff simulation data block. Signal processing techniques (such as filtering, smoothing) and statistical methods (such as mean, standard deviation) are used to extract thrust features and generate a takeoff thrust feature sequence. For example, the engine thrust data in the takeoff stage is extracted, the thrust value per second is calculated, and a thrust time series is generated. The thrust time series is smoothed to remove noise and obtain a stable thrust feature sequence.

[0063] Step S432, perform speed feature recognition on the takeoff simulation data block to obtain a takeoff speed feature sequence. Specifically, data related to speed, such as ground speed, airspeed, speed time series, etc., is extracted from the takeoff simulation data block. Signal processing techniques (such as filtering, smoothing) and statistical methods (such as mean, standard deviation) are used to extract speed features and generate a takeoff speed feature sequence. For example, the ground speed and airspeed data in the takeoff stage are extracted, the speed value per second is calculated, and a speed time series is generated. The speed time series is smoothed to remove noise and obtain a stable speed feature sequence.

[0064] Step S433, align the takeoff thrust feature sequence and the takeoff speed feature sequence to obtain a takeoff thrust-speed feature sequence. Specifically, the thrust feature sequence and the speed feature sequence are aligned on the time axis to ensure that their time points are consistent. The aligned thrust features and speed features are combined into a two-dimensional feature sequence, that is, the takeoff thrust-speed feature sequence. For example, a thrust value and a speed value are recorded per second to form a two-dimensional data point sequence.

[0065] Step S434, perform a search for normal takeoff thrust-speed samples according to the takeoff stage test plan to construct a takeoff thrust-speed reference sequence. Specifically, according to the takeoff stage test plan, normal thrust-speed samples in the takeoff stage are retrieved from historical data or design specifications. The retrieved normal samples are combined into a takeoff thrust-speed reference sequence for risk detection. For example, normal takeoff thrust-speed samples in the takeoff stage are retrieved from historical flight data, and these samples are recorded under standard atmospheric conditions. These normal samples are combined into a reference sequence. For example, the thrust values are [100, 105, 110...], and the speed values are [10, 20, 30...].

[0066] Step S435: Detect the risk of dynamic instability for the takeoff thrust - speed characteristic sequence based on the takeoff thrust - speed reference sequence, obtain the takeoff dynamic instability risk prediction result, and add the takeoff dynamic instability risk prediction result to the takeoff test risk prediction result. Specifically, construct a dynamic instability risk detection model, which can use statistical models (such as regression analysis), machine learning models (such as random forest, neural network), or physical models (such as aerodynamic models). Compare the takeoff thrust - speed characteristic sequence with the takeoff thrust - speed reference sequence to detect whether there are anomalies. Calculate the dynamic instability risk index. For example, when the deviation between the thrust and the speed exceeds a certain threshold, it is determined as a dynamic instability risk. Record the dynamic instability risk prediction result and add it to the takeoff test risk prediction result. For example, use a neural network model to analyze the takeoff thrust - speed characteristic sequence to detect whether there are anomalies. If the thrust value is lower than 10% of the reference sequence and the speed value is lower than 5% of the reference sequence, it is determined as a dynamic instability risk. Record the dynamic instability risk prediction result and add it to the takeoff test risk prediction result.

[0067] This implementation method can accurately detect the dynamic instability risk in the takeoff phase by extracting and aligning the thrust and speed characteristics and comparing them with the reference sequence. This method is more comprehensive and accurate than the risk detection based on a single parameter.

[0068] In a possible implementation, based on the flight test risk prediction factors, perform multi - stage risk prediction on the flight test plan according to the multi - stage simulation data block. Step S430 further includes step S436: Read the takeoff process pressure simulation data and takeoff attitude simulation data according to the takeoff simulation data block. Specifically, read the pressure simulation data (such as air pressure, structural stress, etc.) and attitude simulation data (such as pitch angle, roll angle, yaw angle, etc.) during the takeoff process from the takeoff simulation data block. Pre - process the extracted data to remove noise and outliers to ensure the accuracy and availability of the data. For example, extract the air pressure data in the takeoff phase, with a range of 1010 hPa to 1015 hPa, and extract the pitch angle data in the takeoff phase, with a range of 0 degrees to 10 degrees.

[0069] Step S437: Based on the pressure simulation data during the takeoff process, conduct multi-structural safety risk prediction for the aerospace vehicle to obtain the takeoff structural safety risk prediction result. Specifically, construct a structural safety risk prediction model according to the pressure simulation data during the takeoff process. Finite Element Analysis (FEA) models, statistical models, or machine learning models can be used. Use the model to calculate the structural safety risk index. For example, the ratio of structural stress to material strength. Evaluate the structural safety risk level according to the calculation result. For example, use the finite element analysis model to calculate the structural stress distribution during the takeoff phase, set the material strength threshold to 0.8, and the calculated ratio of the maximum stress to the material strength is 0.75, which is lower than the threshold, so it is determined to be a low risk.

[0070] Step S438: Perform normal attitude fitting according to the test plan during the takeoff phase to determine the takeoff reference attitude data. Specifically, retrieve the normal attitude data from historical data or design specifications according to the test plan during the takeoff phase, perform attitude fitting, and generate the takeoff reference attitude data for attitude deviation risk prediction. For example, retrieve the normal pitch angle data during the takeoff phase from historical flight data, with a range of 0 degrees to 8 degrees, and use the polynomial fitting method to generate the takeoff reference attitude data.

[0071] Step S439: Input the takeoff attitude simulation data and the takeoff reference attitude data into the attitude deviation risk prediction model to obtain the takeoff attitude deviation risk prediction result. Specifically, input the takeoff attitude simulation data and the takeoff reference attitude data into the attitude deviation risk prediction model, and calculate the attitude deviation. For example, pitch angle deviation, roll angle deviation, etc. Evaluate the attitude deviation risk level according to the magnitude of the deviation. For example, input the takeoff attitude simulation data (pitch angle is 10 degrees) and the takeoff reference attitude data (pitch angle is 8 degrees), calculate the pitch angle deviation to be 2 degrees, set the attitude deviation threshold to 3 degrees, and determine it to be a low risk.

[0072] Step S440: Add the takeoff structural safety risk prediction result and the takeoff attitude deviation risk prediction result to the takeoff test risk prediction result. Specifically, integrate the takeoff structural safety risk prediction result and the takeoff attitude deviation risk prediction result into the takeoff test risk prediction result. Conduct a comprehensive evaluation of the integrated risk prediction result to generate the final takeoff test risk prediction report. For example, integrate the structural safety risk prediction result (low risk) and the attitude deviation risk prediction result (low risk) to generate the takeoff test risk prediction report, and the overall risk level is low risk.

[0073] This implementation method more comprehensively evaluates the risk situation during the takeoff phase by combining the predictions of structural safety risk and attitude deviation risk. This method is more comprehensive and accurate than the evaluation of a single risk factor.

[0074] Step S500: Based on the multi-stage test risk prediction results, perform sensitivity correlation on the multi-stage flight simulation environment to obtain multi-stage risk environment sensitivity factors, and adaptively optimize the flight test plan according to the multi-stage risk environment sensitivity factors.

[0075] Specifically, use correlation analysis algorithms (such as Pearson correlation coefficient, Spearman rank correlation coefficient) to perform element correlation on the multi-stage flight simulation environment and the multi-stage test risk prediction results to generate multi-stage risk associated environmental elements. Use perturbation analysis algorithms (such as sensitivity analysis) to evaluate the perturbation effects of multi-stage risk associated environmental elements to generate perturbation evaluation results for each environmental element. Use optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm) to perform optimization analysis on the perturbation evaluation results of each environmental element to generate multi-stage risk environment sensitivity factors, that is, the environmental factors that have the greatest impact on the aircraft risk, such as wind speed, temperature, air pressure, etc. According to the multi-stage risk environment sensitivity factors, use adaptive optimization algorithms (such as dynamic programming, feedback control) to adaptively optimize the flight test plan, that is, dynamically adjust the flight test plan according to the risk environment sensitivity factors to improve the safety and reliability of the aircraft.

[0076] For example, through Pearson correlation coefficient analysis, it is found that the correlation between wind speed at takeoff stage and power instability risk is 0.85. According to the wind speed sensitivity factor, adjust the takeoff speed in the flight test plan from 200 km / h to 220 km / h to reduce the power instability risk.

[0077] In a possible implementation manner, based on the multi-stage test risk prediction results, perform sensitivity correlation on the multi-stage flight simulation environment to obtain multi-stage risk environment sensitivity factors. Step S500 further includes step S510: perform element correlation on the multi-stage test risk prediction results according to the multi-stage flight simulation environment to obtain multi-stage risk associated environmental elements. Specifically, extract the key environmental elements of each stage from the multi-stage flight simulation environment, such as temperature, pressure, wind speed, humidity, etc. Use statistical analysis methods (such as correlation analysis, regression analysis) or machine learning methods (such as association rule mining) to perform correlation analysis on the multi-stage test risk prediction results and environmental elements to generate multi-stage risk associated environmental elements, and clarify which environmental elements have a significant association with the risk prediction results. For example, extract environmental elements such as temperature, pressure, and wind speed at takeoff stage, and use Pearson correlation analysis to find that the wind speed at takeoff stage has a significant positive correlation with the power instability risk (correlation coefficient is 0.85), and generate a list of risk associated environmental elements at takeoff stage, including wind speed, temperature, etc.

[0078] Step S520: Conduct a perturbation impact evaluation on the multi-stage test risk prediction results based on the multi-stage risk-associated environmental factors to obtain the perturbation evaluation results of each environmental factor. Specifically, perform perturbation 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. Use the existing risk prediction model to evaluate the impact of the perturbation on the risk prediction results, generate the perturbation evaluation results of each environmental factor, and clarify which environmental factor perturbations have the greatest impact on the risk. For example, conduct a perturbation simulation on the wind speed during the takeoff stage, increase the wind speed from 5 m / s to 7 m / s, use the risk prediction model to evaluate the dynamic instability risk after the perturbation, find that the risk index increases from 0.1 to 0.3, record the impact of the wind speed perturbation on the dynamic instability risk, and generate the perturbation evaluation result.

[0079] Step S530: Conduct an optimization analysis on the multi-stage risk-associated environmental factors based on the perturbation evaluation results of each environmental factor to generate the multi-stage risk environmental sensitivity factors. Specifically, according to the perturbation evaluation results, use sensitivity analysis methods (such as sensitivity analysis and variance analysis) to analyze the multi-stage risk-associated environmental factors, determine which factors are the most sensitive to the risk, generate the multi-stage risk environmental sensitivity factors, and these factors are the key environmental factors affecting the risk prediction results. Based on the sensitivity factors, propose suggestions for optimizing 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 perturbation of the wind speed during the takeoff stage has the greatest impact on the dynamic instability risk, and the sensitivity is 0.8. Generate the multi-stage risk environmental sensitivity factors for the takeoff stage, including wind speed, temperature, etc., and propose an optimization suggestion: increase the number of tests under high wind speed conditions to evaluate the wind resistance ability of the aircraft.

[0080] This implementation method can accurately identify the environmental factors that have the greatest impact on the risk, namely the multi-stage risk environmental sensitivity factors, through factor association, perturbation impact evaluation, and optimization analysis, which helps to more accurately understand the source and impact mechanism of the risk. According to the sensitivity factors, the flight test plan can be optimized accordingly. For example, if the wind speed is a sensitivity factor, more tests can be conducted under different wind speed conditions to evaluate the wind resistance ability of the aircraft. This kind of optimization can improve the efficiency and reliability of the test. By identifying the sensitivity factors and optimizing the test plan, potential risk points can be discovered in advance, and corresponding measures (such as adjusting the test conditions, increasing safety inspections) can be taken to reduce the risk and improve the safety of the flight test. The identification of the sensitivity factors can help the aircraft design team better understand the performance of the aircraft under different environmental conditions, so as to optimize the design of the aircraft and make it more adaptable.

[0081] In the embodiments of the present application, according to the flight test plan, an atmospheric data system tester is used to construct a multi-stage flight simulation environment, and tests are carried out in the simulation environment to generate the first sequence of flight simulation tests. The first sequence of tests is compensated for environmental variations to generate a second sequence. Based on the second sequence, multi-dimensional risk prediction is carried out to generate a risk prediction result. The risk prediction result is analyzed to identify sensitive factors, and the flight test plan is optimized, etc. These technical means solve the technical problems existing in the adaptability test of existing aerospace vehicles in different atmospheric environments, such as the limitation of environmental simulation scenarios and the lack of consideration of uncertain factors, resulting in incomplete and inaccurate test results, and achieve the technical effects of truly and comprehensively simulating the flight state of aerospace vehicles in complex atmospheric environments and improving the comprehensiveness and accuracy of test results.

[0082] In the above text, reference is made to Figure 1 A method for simulating an atmospheric environment for aerospace flight simulation according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe an apparatus for simulating an atmospheric environment for aerospace flight simulation according to an embodiment of the present invention.

[0083] An apparatus for simulating an atmospheric environment for aerospace flight simulation according to an embodiment of the present invention is used to solve the technical problems existing in the adaptability test of existing aerospace vehicles in different atmospheric environments, such as the limitation of environmental simulation scenarios and the lack of consideration of uncertain factors, resulting in incomplete and inaccurate test results, and achieve the technical effects of truly and comprehensively simulating the flight state of aerospace vehicles in complex atmospheric environments and improving the comprehensiveness and accuracy of test results. An apparatus for simulating an atmospheric environment 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 correlation module 50.

[0084] The multi-stage environment construction module 10 is used to construct a multi-stage environment according to the flight test plan of the aerospace vehicle by using an atmospheric data system tester to obtain a multi-stage flight simulation environment; the simulation test module 20 is used to perform a simulation test on the aerospace vehicle based on the flight test plan and according to the multi-stage flight simulation environment to obtain the first sequence of flight simulation tests; the 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 the second sequence of flight simulation tests; the multi-dimensional risk prediction module 40 is used to perform multi-dimensional risk prediction on the flight test plan based on flight test risk prediction factors and according to the second sequence of flight simulation tests to obtain a multi-stage test risk prediction result; the sensitivity correlation module 50 is used to perform sensitivity correlation on the multi-stage flight simulation environment according to the multi-stage test risk prediction result to obtain multi-stage risk environment sensitive factors, and adaptively optimize the flight test plan according to the multi-stage risk environment sensitive factors.

[0085] Next, the specific configuration of the simulation test module 20 will be described in detail. 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 the first sequence of flight simulation tests. The simulation test module 20 may further include: a modeling unit for modeling according to the aerospace vehicle to obtain an aircraft model; a multi-stage test importance evaluation unit for evaluating the importance of multi-stage tests according to the flight test plan to obtain the importance degrees of tests in each stage, and constructing a multi-stage simulation test confidence mechanism according to the importance degrees of tests in each stage; a multi-stage test unit for performing multi-stage tests 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 according to the multi-stage test data set to generate the first sequence of flight simulation tests.

[0086] Next, the specific configuration of the simulation scenario mutation compensation module 30 will be described in detail. As described above, the first sequence of flight simulation tests is subjected to simulation scenario mutation compensation according to the multi-stage flight simulation environment to obtain the second sequence of flight simulation tests. The simulation scenario mutation compensation module 30 may further include: a simulation test scenario information collection unit for collecting simulation test scenario information according to the first sequence of flight simulation tests to obtain a multi-stage simulation test scenario; a mutation identification unit for identifying mutations in the multi-stage simulation test scenario according to the multi-stage flight simulation environment to obtain mutation identification results for the environment in each stage; an impact analysis unit for analyzing the impact on the first sequence of flight simulation tests according to the mutation identification results for the environment in each stage to obtain impact analysis results for mutations in each stage; and a correction unit for correcting the first sequence of flight simulation tests according to the impact analysis results for mutations in each stage to generate the second sequence of flight simulation tests.

[0087] 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 factors, multi-dimensional risk prediction of the flight test plan is performed according to the second sequence of flight simulation tests to obtain multi-stage test risk prediction results. 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 flight simulation tests, the multi-stage simulation data block including 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 flight test risk prediction factors, the flight test risk prediction factors including 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 factors to generate the multi-stage test risk prediction results, the multi-stage test risk prediction results including 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.

[0088] Among them, based on the flight test risk prediction factors, multi-stage risk prediction of the flight test plan is performed according to the multi-stage simulation data block. The multi-stage risk prediction unit may further include: a thrust characteristic identification subunit for performing thrust characteristic identification according to the takeoff simulation data block to obtain a takeoff thrust characteristic sequence; a speed characteristic identification subunit for performing speed characteristic identification on the takeoff simulation data block to obtain a takeoff speed characteristic sequence; an alignment subunit for aligning the takeoff thrust characteristic sequence and the takeoff speed characteristic sequence to obtain a takeoff thrust-speed characteristic sequence; a normal sample retrieval subunit for performing takeoff thrust-speed normal sample retrieval according to the takeoff stage 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 characteristic 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.

[0089] Among them, 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 block. The multi-stage risk prediction unit may further include: a simulation data reading sub-unit for reading the take-off process pressure simulation data and the take-off attitude simulation data according to the take-off simulation data block; a multi-structure safety risk prediction sub-unit for performing a multi-structure safety risk prediction on the aerospace vehicle according to the take-off process pressure simulation data to obtain a take-off structure safety risk prediction result; a normal attitude fitting sub-unit for performing a normal attitude fitting according to the take-off stage test plan to determine the take-off reference attitude data; an attitude deviation risk prediction sub-unit for inputting the take-off attitude simulation data and the take-off reference attitude data into an attitude deviation risk prediction model to obtain a take-off attitude deviation risk prediction result; and a result adding sub-unit for adding the take-off structure safety risk prediction result and the take-off attitude deviation risk prediction result to the take-off test risk prediction result.

[0090] Next, the specific configuration of the sensitivity association module 50 will be described in detail. As described above, according to the multi-stage test risk prediction result, a sensitivity association is performed on the multi-stage flight simulation environment to obtain a multi-stage risk environment sensitivity factor. The sensitivity association module 50 may further include: an element association unit for performing an element association on the multi-stage test risk prediction result according to the multi-stage flight simulation environment to obtain multi-stage risk associated environmental elements; a perturbation impact evaluation unit for performing a perturbation impact evaluation on the multi-stage test risk prediction result according to the multi-stage risk associated environmental elements to obtain a perturbation evaluation result for each environmental element; and an optimization analysis unit for performing an optimization analysis on the multi-stage risk associated environmental elements according to the perturbation evaluation result for each environmental element to generate the multi-stage risk environment sensitivity factor.

[0091] Next, the specific configuration of the multi-stage environment construction module 10 will be described in detail. As described above, a multi-stage environment construction is performed on the flight test plan of the aerospace vehicle according to the atmospheric data system tester to obtain a multi-stage flight simulation environment. The multi-stage environment construction module 10 may further include: a flight stage decomposition unit for decomposing the flight stage according to the flight test plan to obtain a multi-stage flight test plan, where 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; an environment prediction unit for performing an environment prediction according to 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 according to the atmospheric data system tester.

[0092] Among them, based on the multi-stage flight prediction environment, the multi-stage flight simulation environment is constructed according to the atmospheric data system tester. The multi-stage flight simulation environment construction unit may further include: a multi-stage flight construction environment acquisition subunit for obtaining a 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.

[0093] The atmospheric environment simulation device for aerospace flight simulation provided by the embodiments of the present invention can execute the atmospheric environment simulation method for aerospace flight simulation provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0094] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server. The included various units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0095] The above specific embodiments do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

Claims

1. An atmospheric environment simulation method for aerospace flight simulation, characterized in that, The method includes: Constructing multi-stage environments according to the flight test plan of the aerospace vehicle by an atmospheric data system tester to obtain a multi-stage flight simulation environment; Based on the flight test plan, simulating and testing the aerospace vehicle according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests; Performing simulated scenario mutation 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 flight test risk prediction factors, performing multi-dimensional risk prediction on the flight test plan according to the second sequence of flight simulation tests to obtain multi-stage test risk prediction results; Performing sensitivity association on the multi-stage flight simulation environment according to the multi-stage test risk prediction results to obtain multi-stage risk environment sensitivity factors, and adaptively optimizing the flight test plan according to the multi-stage risk environment sensitivity factors.

2. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, wherein Based on the flight test plan, simulating and testing the aerospace vehicle according to the multi-stage flight simulation environment to obtain a first sequence of flight simulation tests, including: Modeling the aerospace vehicle to obtain an aircraft model; Performing multi-stage test importance evaluation according to the flight test plan to obtain the importance degree of each stage of the test, and constructing a multi-stage simulation test confidence mechanism according to the importance degree of each stage of the test; Based on the multi-stage simulation test confidence mechanism, performing multi-stage tests 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; Performing confidence fusion according to the multi-stage test data set to generate the first sequence of flight simulation tests.

3. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that Performing simulated scenario mutation 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 simulated test scenario information according to the first sequence of flight simulation tests to obtain multi-stage simulated test scenarios; Identifying mutations in the multi-stage simulated test scenarios according to the multi-stage flight simulation environment to obtain mutation identification results for each stage of the environment; Analyzing the influence on the first sequence of flight simulation tests according to the mutation identification results for each stage of the environment to obtain influence analysis results for each stage of the mutation; Correcting the first sequence of flight simulation tests according to the influence analysis results for each stage of the mutation 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 flight test risk prediction factors, performing multi-dimensional risk prediction on the flight test plan according to the second sequence of flight simulation tests to obtain multi-stage test risk prediction results, including: Constructing multi-stage simulated data blocks according to the second sequence of flight simulation tests, where the multi-stage simulated data blocks include takeoff simulation data blocks, climb simulation data blocks, cruise simulation data blocks, reentry simulation data blocks, and landing simulation data blocks; 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 factors, perform multi-stage risk prediction on the flight test plan according to the multi-stage simulation data block, and generate the multi-stage test risk prediction results, where the multi-stage test risk prediction results include takeoff test risk prediction results, climb test risk prediction results, cruise test risk prediction results, re-entry test risk prediction results, and landing test risk prediction results.

5. The atmospheric environment simulation method for aerospace flight simulation according to claim 4, characterized in that Based on the flight test risk prediction factors, perform multi-stage risk prediction on the flight test plan according to the multi-stage simulation data block, including: Perform thrust characteristic identification according to the takeoff simulation data block to obtain a takeoff thrust characteristic sequence; Perform speed characteristic identification on the takeoff simulation data block to obtain a takeoff speed characteristic sequence; Align the takeoff thrust characteristic sequence and the takeoff speed characteristic sequence to obtain a takeoff thrust-speed characteristic sequence; Retrieve normal samples of takeoff thrust-speed according to the takeoff phase test plan to construct a takeoff thrust-speed reference sequence; Perform dynamic instability risk detection on the takeoff thrust-speed characteristic sequence according to the takeoff thrust-speed reference sequence to obtain a takeoff dynamic instability risk prediction result, and add the takeoff dynamic instability risk prediction result 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 factors, perform multi-stage risk prediction on the flight test plan according to the multi-stage simulation data block, including: Read the takeoff process pressure simulation data and takeoff attitude simulation data according to the takeoff simulation data block; Perform multi-structure safety risk prediction on the aerospace vehicle according to the takeoff process pressure simulation data to obtain a takeoff structural safety risk prediction result; Perform normal attitude fitting according to the takeoff phase test plan to determine the takeoff reference attitude data; Input the takeoff attitude simulation data and the takeoff reference attitude data into the attitude deviation risk prediction model to obtain a takeoff attitude deviation risk prediction result; Add the takeoff structural safety risk prediction result and the takeoff attitude deviation risk prediction result to the takeoff test risk prediction result.

7. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that, Perform sensitivity correlation on the multi-stage flight simulation environment according to the multi-stage test risk prediction results to obtain multi-order risk environment sensitivity factors, including: Perform element correlation on the multi-stage test risk prediction results according to the multi-stage flight simulation environment to obtain multi-stage risk-related environmental elements; Perform perturbation impact evaluation on the multi-stage test risk prediction results according to the multi-stage risk-related environmental elements to obtain perturbation evaluation results of each environmental element; Perform optimization analysis on the multi-stage risk-related environmental elements according to the perturbation evaluation results of each environmental element to generate the multi-order risk environment sensitivity factors.

8. The atmospheric environment simulation method for aerospace flight simulation according to claim 1, characterized in that Perform multi-stage environment construction on the flight test plan of the aerospace vehicle according to the air data system tester to obtain a multi-stage flight simulation environment, including: Conduct flight phase disassembly according to the flight test plan to obtain a multi-phase flight test plan, where the multi-phase flight test plan includes a takeoff phase test plan, a climb phase test plan, a cruise phase test plan, a re-entry phase test plan, and a landing phase test plan; Conduct environmental prediction according to the multi-phase flight test plan to obtain a multi-phase flight prediction environment; Based on the multi-phase flight prediction environment, construct the multi-phase flight simulation environment according to the atmospheric data system tester.

9. The atmospheric environment simulation method for aerospace flight simulation according to claim 8, wherein, Based on the multi-phase flight prediction environment, construct the multi-phase flight simulation environment according to the atmospheric data system tester, including: Based on the multi-phase flight prediction environment, obtain a multi-phase flight construction environment according to the atmospheric data system tester; Dynamically compare the multi-phase flight prediction environment and the multi-phase flight construction environment to obtain a multi-phase environment comparison result, and correct the multi-phase flight construction environment according to the multi-phase environment comparison result to obtain the multi-phase 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 1-9, and the device includes: A multi-phase environment construction module, configured to perform multi-phase environment construction on the flight test plan of an aerospace vehicle according to an atmospheric data system tester to obtain a multi-phase flight simulation environment; A simulation test module, configured to perform a simulation test on the aerospace vehicle based on the flight test plan according to the multi-phase flight simulation environment to obtain a first sequence of flight simulation tests; A simulation scenario mutation compensation module, configured to perform simulation scenario mutation compensation on the first sequence of flight simulation tests according to the multi-phase flight simulation environment to obtain a second sequence of flight simulation tests; A multi-dimensional risk prediction module, configured to perform multi-dimensional risk prediction on the flight test plan based on flight test risk prediction factors according to the second sequence of flight simulation tests to obtain a multi-phase test risk prediction result; A sensitivity correlation module, configured to perform sensitivity correlation on the multi-phase flight simulation environment according to the multi-phase test risk prediction result to obtain a multi-order risk environment sensitivity factor, and perform adaptive optimization on the flight test plan according to the multi-order risk environment sensitivity factor.

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