Flight simulation system and method based on test data

Abstract

The invention relates to the technical field of computer simulation, in particular to a flight simulation system and method based on test data, and the system comprises an aircraft parameter calibration module, a task environment correlation analysis module, a flight path modeling module and a control strategy optimization module. According to the method, the precision and the adaptability of flight parameter data are improved by extracting and correcting the dynamic parameters, the attitude parameters and the environment variables of the aircraft test data, the matching precision of a task scene and a test environment is remarkably improved, and the dynamic flight path curve is generated by classifying and marking the trajectory points of the dense disturbance region and generating the dynamic flight path curve. The prediction and simulation capability of the trajectory in the complex flight environment is enhanced, the disturbance environment optimization control logic is combined, the control scheme optimization driven by test data is completed, the flight control precision and the adaptive capacity in the complex scene are remarkably improved, the closed loop from data calibration to control strategy optimization is realized, the task execution capability and the safety are enhanced, and the flight control precision and the adaptive capacity in the complex scene are improved. And meanwhile, research and development and operation risks are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of computer simulation, and in particular to a flight simulation system and method based on test data. Background Art

[0002] The field of computer simulation technology uses computer models to imitate real systems or processes. It is widely used in engineering design, scientific research, education and training, and decision support. This technology simulates and predicts the dynamic behavior of complex systems by establishing mathematical models, physical models, or intelligent models, combined with computer algorithms and high-performance computing resources. Computer simulation technology is efficient, flexible, and safe, and can provide scientific basis and optimization support for research and development when real experimental conditions are difficult to achieve or costly. In complex scenarios such as aerospace, medical health, and energy management, this technology particularly shows great value and development potential.

[0003] Among them, the flight simulation system is the specific application of computer simulation technology in the aviation field. It is mainly used in scenarios such as pilot training, aircraft design verification and mission planning. The system helps pilots improve their operating skills by simulating the flight environment, aircraft characteristics and mission operation processes, supports aircraft performance verification in the design stage, and formulates the best plan for specific tasks. The flight simulation system has the advantages of low cost, low risk and strong repeatability, and has played an important role in ensuring flight safety, improving flight efficiency and reducing R&D costs.

[0004] When processing aircraft test data, the accuracy of parameter calibration is limited by the integrity and uniformity of the basic data, and it is difficult to effectively deal with the complex relationship between multiple variables, resulting in insufficient adaptability and credibility of test data in multiple environmental scenarios. In terms of mission environment and disturbance factor analysis, the existing technologies mostly use simple statistical or rule mapping methods, and fail to fully consider the complex dynamic association between disturbance factors and mission data in different time periods, which easily leads to limitations in environmental modeling and task matching. The existing technologies have limited simulation capabilities for dynamic changes in flight trajectories and disturbance distribution, and lack fine marking and dynamic adjustment of disturbance-intensive areas, which affects the accuracy and adaptability of trajectory models. In terms of control strategy design, the existing technologies mostly rely on preset control logic, have insufficient adaptability to complex environments, and fail to fully utilize the dynamic association characteristics in test data for optimization, resulting in problems such as decreased control accuracy, low mission execution efficiency, and increased safety risks in aircraft in complex mission scenarios, while increasing the R&D cost and time consumption in the process of aircraft design verification and mission planning. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a flight simulation system and method based on test data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: The flight simulation system based on test data includes:

[0007] The aircraft parameter calibration module extracts power parameters, attitude parameters, and test environment variables based on the basic data of aircraft tests, performs interpolation correction and flight phase parameter normalization, adjusts the change trend of test environment variables, and generates a calibration flight parameter set;

[0008] The mission environment correlation analysis module extracts the aircraft mission data and the test environment disturbance factors based on the calibration flight parameter set, analyzes the change range and correlation strength of the mission data and the disturbance factors in the differentiated time periods, generates a mission environment correlation matrix, screens the associated disturbance factors and sorts them by time series weight, and maps the disturbance distribution to the mission phase to generate a mission environment matching table;

[0009] The flight trajectory modeling module analyzes the three-dimensional position change characteristics of the trajectory points and the disturbance distribution area in the test data based on the mission environment matching table, generates a flight trajectory distribution result, classifies and labels the trajectory points in the disturbance-intensive area, and simulates the dynamic flight trajectory curve in combination with the disturbance data to generate a flight trajectory simulation model;

[0010] Based on the flight trajectory simulation model, the control strategy optimization module extracts the attitude point parameters and trajectory control variables in the simulated trajectory, optimizes the control variables and the attitude point time series weight distribution, matches the disturbance environment, adjusts the flight control logic, defines flight instructions, and generates a flight control plan driven by test data.

[0011] As a further solution of the present invention, the step of obtaining the calibration flight parameter set is specifically:

[0012] Based on the basic data of the aircraft test, the power parameters, attitude parameters and test environment variables are called, the change trend of the test environment variables is normalized in sections, the change rate in each section is calculated, and the weighted change rate set of attitude parameters is obtained by mapping the change rate with the attitude parameters;

[0013] Call the weighted change rate set of the posture parameters, set the weight parameters of the segments and associate them with the weighted change rate, using the formula:

[0014]

[0015] Generate normalized parameter results for flight phase;

[0016] Among them, P norm represents the normalized value of the flight phase parameter, V i is the weighted change rate of the posture parameters of the i-th segment, W iis the weight parameter, n represents the total number of segments;

[0017] Based on the normalized parameter results of the flight phase, the segmented normalized change rate set of the test environment variables is called to dynamically correct the normalized parameters. By comparing the normalized differences of the parameters in each phase, adjusting the weight abnormal items, and eliminating data with large deviations, a calibrated flight parameter set is obtained.

[0018] As a further solution of the present invention, the step of obtaining the task environment association matrix is ​​specifically as follows:

[0019] Based on the calibration flight parameter set, the aircraft mission data and the test environment disturbance factor are called, the variation range of the mission data and the disturbance factor is extracted in segments according to the time series, each variation value is normalized, and a preliminary variation range set of the mission data and the disturbance factor is generated by preliminary weighted operation of the normalized data;

[0020] The preliminary variation range set of the task data and the disturbance factor is called, and the correlation strength between the task data and the disturbance factor within the time period is calculated using the formula:

[0021]

[0022] Generate a list of strengths of association;

[0023] Among them, R represents the correlation strength value between task data and disturbance factor, T j is the task data variation range in the jth time period, E j is the range of disturbance factor variation in the jth time period, F j is the weight coefficient of the jth time period, C is the smoothing adjustment parameter, and m represents the total number of time periods;

[0024] The association strength list is called to filter the matrix elements whose association strength is higher than a set threshold within a time period, and the task environment association matrix is ​​output by accumulating and integrating the association strength list row by row and column by column.

[0025] As a further solution of the present invention, the step of obtaining the task environment matching table is specifically as follows:

[0026] Based on the task-environment correlation matrix, the disturbance factors in the task that are correlated with the environment are screened, the influence ratio of each disturbance factor on the task execution is identified, the disturbance factors are weighted and ranked, and a disturbance factor weight ranking table is obtained;

[0027] Based on the disturbance factor weight ranking table, the top-ranked disturbance factors are grouped one by one according to the time series, the time position of each disturbance factor is divided, the weight accumulation of the disturbance factors in the time node is calculated and mapped to the task cycle time point, and the disturbance factor time distribution table is generated;

[0028] Based on the disturbance factor time distribution table, the task cycle is divided into multiple stages, the task stages are matched with the disturbance factor time distribution, the weighted sum of the disturbance factor weights within the stage is calculated, the task cycle disturbance distribution and the stage association are integrated, and a task environment matching table is generated.

[0029] As a further solution of the present invention, the step of obtaining the flight trajectory distribution result is specifically:

[0030] According to the task environment matching table, the coordinate parameters of the three-dimensional trajectory points in the test data are extracted, the displacement change of the trajectory points in the three-dimensional space and the distribution characteristics of the disturbance area are analyzed by the coordinate parameters, the concentrated distribution of the disturbance range is judged by the position parameters between the trajectory points, and the matching results of the trajectory point position change and the disturbance area are generated;

[0031] Based on the matching results of the trajectory point position change and the disturbance area, the position offset of the trajectory point in the disturbance area is analyzed, the disturbance influence parameter and the position deviation are called, and the correction coefficient and the nonlinear weight parameter are adjusted, and the formula is adopted:

[0032]

[0033] Calculate the corrected trajectory point positions and obtain the corrected trajectory position distribution results;

[0034] in, represents the corrected trajectory point position, X represents the original trajectory point position, δ is the position correction coefficient, D represents the distance from the trajectory point to the center of the disturbance area, ΔX represents the position deviation, ΔR represents the disturbance influence parameter, and γ is the nonlinear weight parameter, which controls the correlation between the correction range and the position deviation;

[0035] Based on the position distribution result after trajectory correction, the disturbance area parameters and the target area parameters of the mission environment matching table are called, the distribution density and concentration of the corrected trajectory points in the target area are analyzed, and the coverage and range of the trajectory distribution area are judged by statistically analyzing the distribution characteristics of the trajectory points to generate the flight trajectory distribution result.

[0036] As a further solution of the present invention, the step of acquiring the flight trajectory simulation model is specifically as follows:

[0037] Based on the flight trajectory distribution results, the coordinates and time series of the trajectory points in the disturbance-intensive area are extracted, the trajectory points are grouped and counted according to the coordinate spacing, the coordinate value and time parameter density are called, the trajectory points of the high-density group are marked, and a classification and marking table of trajectory points in the disturbance-intensive area is generated;

[0038] Based on the disturbance intensive area trajectory point classification and annotation table, the coordinate data and time nodes of the annotated trajectory points are extracted, the disturbance amplitude data are matched with the trajectory points, the coordinate and time series parameters are called to linearly interpolate the disturbance amplitudes between the trajectory points, and a dynamic trajectory point distribution table of the disturbance data is constructed;

[0039] Based on the dynamic trajectory point distribution table of the disturbance data, the coordinate sequence of continuous dynamic trajectory points is connected, the trajectory curve is reorganized through the time node sequence, the curve interval and direction change between trajectory points are adjusted, the dynamic trend of the curve is calibrated, and a flight trajectory simulation model is generated.

[0040] As a further solution of the present invention, the steps of obtaining the flight control solution driven by the test data are specifically as follows:

[0041] Based on the flight trajectory simulation model, the attitude point parameters in the simulation trajectory are extracted, and the three-dimensional coordinates and time series distribution of the attitude points are analyzed point by point, the attitude point parameters and trajectory control variables are called, and the change rate on the time axis is calculated. By comparing the change amount of each attitude point between consecutive time nodes, the time series change trend of the attitude point parameters is obtained;

[0042] According to the time series change trend of the attitude point parameters, the trajectory control variable parameters are called, and the response amplitude of the control variable and the attitude point change rate are adjusted through weight distribution optimization, using the formula:

[0043]

[0044] Calculate the time series weight value of the control variable and generate the optimized control variable and attitude point time series weight distribution result;

[0045] Among them, W k represents the time series weight value of the control variable, P k represents the initial value of the control variable, Q k Indicates the rate of change of attitude points, T k represents the time threshold parameter, C k represents the trajectory disturbance correlation coefficient, α represents the time series distribution weight coefficient;

[0046] Based on the optimized control variables and attitude point time series weight distribution results, the disturbance environment parameters are matched, the matching degree of the control variables and the disturbance environment is analyzed, the flight control logic is optimized in combination with the attitude point time series distribution, the flight instructions are defined and the response amplitude of the control variables at the time nodes is adjusted, and the flight control scheme driven by the test data is generated.

[0047] The flight simulation method based on test data is performed based on the flight simulation system based on test data, and comprises the following steps:

[0048] S1: Based on the basic data of the aircraft test, extract the power parameters, attitude parameters and test environment variables, remove the mutation data, perform time normalization on the attitude parameters, remove the abnormal fluctuation items and interference values, and generate the calibration flight parameter set;

[0049] S2: Based on the calibration flight parameter set, extract the aircraft mission data and the test environment disturbance factors, analyze the correlation coefficient and the variation range, sort the disturbance factors by time point, match the mission phase data, and generate a mission environment matching table;

[0050] S3: Based on the task environment matching table, extract the three-dimensional position change parameters of the trajectory points, classify them into disturbance areas, analyze the trend and density distribution of the trajectory points in the disturbance-intensive areas, match the disturbance weights of the time periods, and generate the flight trajectory distribution results;

[0051] S4: based on the flight trajectory distribution result, extract the time series and attitude parameters of the trajectory points in the disturbance-intensive area, segmentally fit the trajectory point positions and attitude parameters, match the disturbance factor weights, and generate a flight trajectory simulation model;

[0052] S5: Based on the flight trajectory simulation model, attitude point parameters and trajectory control variables are extracted, attitude point time series are numerically segmented and analyzed, disturbance area time points are matched, control variables and attitude point parameters are optimized and integrated, and a flight control scheme driven by experimental data is generated.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] In the present invention, the accuracy and adaptability of flight parameter data are significantly improved by extracting and interpolating the power parameters, attitude parameters and test environment variables of the basic data of the aircraft test, and the matching accuracy of the mission scene and the test environment is significantly improved by analyzing the change range and correlation strength of the mission data and the environmental disturbance factors in the differentiated time periods, and the problem that the correlation of parameters in the complex environment is difficult to quantify is solved. By classifying and labeling the trajectory points in the disturbance-intensive area and generating the dynamic flight trajectory curve, the prediction and simulation capabilities of the flight trajectory in the complex flight environment are improved, and the trajectory model is made more adaptable and accurate in the dynamic environment. In the optimization of the flight control strategy, the attitude point parameters and control variables in the simulated trajectory are extracted, and the control logic is adjusted in combination with the disturbance environment to complete the optimization of the flight control scheme driven by the test data, which greatly improves the control accuracy and adaptability of the aircraft in the complex mission scene, and realizes a complete closed loop from data calibration, environmental correlation analysis, trajectory simulation to control strategy optimization. With multi-dimensional data analysis and dynamic modeling as the core, the mission execution capability and safety performance of the aircraft in the complex mission scene are significantly enhanced, and the research and development and operation risks caused by the uncertainty of the test environment are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a system flow chart of the present invention;

[0056] Figure 2 A flow chart for calibrating a flight parameter set in the present invention;

[0057] Figure 3 It is a flow chart of the task environment association matrix in the present invention;

[0058] Figure 4 It is a flowchart of the task environment matching table in the present invention;

[0059] Figure 5 A flow chart of the flight trajectory distribution results in the present invention;

[0060] Figure 6 It is a flow chart of the flight trajectory simulation model in the present invention;

[0061] Figure 7 Flowchart of the experimental data-driven flight control scheme in the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0064] See also Figure 1 The present invention provides a technical solution: a flight simulation system based on test data includes:

[0065] The aircraft parameter calibration module extracts power parameters, attitude parameters, and test environment variables based on the basic data of aircraft tests, performs interpolation correction and flight phase parameter normalization, adjusts the change trend of test environment variables, and generates a calibration flight parameter set;

[0066] The mission environment correlation analysis module extracts the aircraft mission data and test environment disturbance factors based on the calibration flight parameter set, analyzes the change range and correlation strength of the mission data and disturbance factors in differentiated time periods, generates a mission environment correlation matrix, screens the associated disturbance factors and sorts them by time series weight, and maps the disturbance distribution to the mission phase to generate a mission environment matching table;

[0067] The flight trajectory modeling module analyzes the three-dimensional position change characteristics of the trajectory points and the disturbance distribution area in the test data based on the mission environment matching table, generates the flight trajectory distribution results, classifies and labels the trajectory points in the disturbance-intensive area, and simulates the dynamic flight trajectory curve in combination with the disturbance data to generate a flight trajectory simulation model;

[0068] The control strategy optimization module is based on the flight trajectory simulation model, extracts the attitude point parameters and trajectory control variables in the simulated trajectory, optimizes the weight distribution of the control variables and attitude point time series, matches the disturbance environment, adjusts the flight control logic, defines flight instructions, and generates a flight control plan driven by experimental data.

[0069] The calibration flight parameter set includes power parameters, attitude parameters, test environment variables, and flight phase parameters. The mission environment association matrix includes the mission data variation range, disturbance factor variation range, and correlation strength. The mission environment matching table includes associated disturbance factors, disturbance distribution areas, and time series weights. The flight trajectory distribution results include three-dimensional position change characteristics, disturbance intensive areas, and trajectory point classification and labeling. The flight trajectory simulation model includes dynamic flight trajectory curves and disturbance intensive area distribution. The test data-driven flight control scheme includes control variable optimization distribution, attitude point parameter weight distribution, and flight control logic adjustment.

[0070] See also Figure 2 , the specific steps for obtaining the calibration flight parameter set are:

[0071] Based on the basic data of the aircraft test, the power parameters, attitude parameters and test environment variables are called, the change trend of the test environment variables is normalized in sections, the change rate in each section is calculated, and the weighted change rate set of attitude parameters is obtained by mapping the change rate with the attitude parameters;

[0072] The dynamic parameters, attitude parameters and test environment variables of different time periods in the test data record library are called, and the test environment variable data are segmented according to the time series. The change amplitude and change rate of the data in each segment are calculated. The maximum and minimum values ​​of the segmented data are firstly differentially calculated, and then the change range is divided by the time span to obtain the change rate of each segment. Subsequently, the change rate is mapped to the [0, 1] interval using a normalization function, and the normalized segmented environmental variable data is accumulated to obtain the normalized segmented change trend result. Then, the acceleration, angular velocity and height data in the attitude parameter data set are called, and the normalized change trend of each segment is associated with the attitude parameter data point. The weighted change rate of the attitude parameter in the corresponding time period is extracted to generate a set of weighted change rates of the attitude parameters.

[0073] Call the weighted change rate set of attitude parameters, set the weight parameters of the segment and associate them with the weighted change rate, using the formula:

[0074]

[0075] Generate normalized parameter results for flight phase;

[0076] Among them, P norm represents the normalized value of the flight phase parameter, V i is the weighted change rate of the posture parameters of the i-th segment, W i is the weight parameter, n represents the total number of segments;

[0077] The benefit of the formula is that by introducing the absolute value of the weight parameter and the weighted change rate of the posture parameter, and combining the segmented normalization results with the square root normalization, the uneven distribution of parameter weights is improved, and the calculation flexibility and accuracy of normalization are improved;

[0078] Assume the number of segments is n = 3, and the weighted change rate of the posture parameter V i ={0.2, 0.4, 0.5}, weight parameter W i ={0.6, 0.8, 1.0};

[0079] Calculate the numerator part:

[0080] Calculate the denominator:

[0081]

[0082]

[0083] Combined denominators:

[0084] Substituting into the formula:

[0085] This result shows that the calculated P norm =0.45 is the normalized calibration parameter value, which represents the intermediate result after weighted normalization of the segmented posture parameters and weight parameters, and can be used to further dynamically correct the parameter distribution of each segment.

[0086] Based on the normalized parameter results of the flight phase, the segmented normalized change rate set of the test environment variables is called to dynamically calibrate the normalized parameters. By comparing the normalized differences of the parameters in each phase, adjusting the weight abnormal items, and eliminating the data with large deviations, the calibrated flight parameter set is obtained.

[0087] First, the normalized parameter results are segmented and differentiated to extract the normalized difference values ​​between the segments. Then, the threshold range is set to judge the extracted results item by item, and the segmented parameter sets whose deviations exceed the threshold are screened. The segmented change rate data of the test environment variables are called to dynamically adjust the segmented parameters with deviations. The deviation item data are redistributed by introducing a dynamic correction coefficient, and the deviation item data are evenly distributed to adjacent segments. At the same time, data cleaning is performed on the segmented data set after weight adjustment to eliminate abnormal data points that deviate from the normal distribution range, recalculate the adjusted normalized parameter difference values, and merge the adjusted normalized parameter data with the remaining segmented normalized parameter sets. Finally, the output calibration flight parameter set is sorted out.

[0088] See also Figure 3, the specific steps for obtaining the task-environment association matrix are:

[0089] Based on the calibration flight parameter set, the aircraft mission data and the test environment disturbance factor are called, the variation range of the mission data and the disturbance factor is extracted in segments according to the time series, and each segment of the variation value is normalized. Through the preliminary weighted operation of the normalized data, a preliminary variation range set of the mission data and the disturbance factor is generated;

[0090] First, the time series is divided into several time windows, and the maximum and minimum values ​​of the task data and the disturbance factor in each time period are extracted. The difference between the two in the time period is calculated respectively, and then the variation range is normalized, and the variation range of the task data and the disturbance factor is mapped to the [0, 1] interval. The normalization process is completed by dividing the difference by the maximum data amplitude in the time period to generate a set of normalized variation values ​​for each time period. Then, the normalized data is subjected to segmented accumulation processing. The cumulative variation trend of the task data and the disturbance factor is obtained by continuous summation on the time series. At the same time, the segmented data is subjected to time weighted operation to adjust the contribution of each segment of data in the final variation range set to ensure the correlation integrity of the variation range set in the time period dimension. Finally, a preliminary variation range set of the task data and the disturbance factor is formed for the next step of correlation strength analysis.

[0091] Call the initial range of changes in task data and disturbance factors, and calculate the correlation strength between task data and disturbance factors within the time period using the formula:

[0092]

[0093] Generate a list of strengths of association;

[0094] Among them, R represents the correlation strength value between task data and disturbance factor, T j is the task data variation range in the jth time period, E j is the range of disturbance factor variation in the jth time period, F j is the weight coefficient of the jth time period, C is the smoothing adjustment parameter, and m represents the total number of time periods;

[0095] The benefit of the formula is that by introducing the time period weighting coefficient and the smoothing adjustment parameter, the dynamic change characteristics of the task data and the disturbance factor are quantified into a calculable correlation strength, which enhances the accuracy of the correlation evaluation between the data;

[0096] Assume that the number of time periods m = 3, and the task data variation range is T j ={1.2, 0.8, 1.0}, the disturbance factor variation range E j ={0.9, 1.1, 0.7}, time period weighting coefficient Fj ={0.6, 0.8, 0.5}, smoothing adjustment parameter C = 0.1;

[0097] Calculate the numerator part:

[0098]

[0099] Calculate the denominator:

[0100]

[0101] Substituting into the formula:

[0102] The result shows that the calculated R=0.501 is the correlation strength value between the task data and the disturbance factor, indicating that the dynamic change characteristics of the two within the time period have a medium-strength correlation, which serves as the core calculation result for generating the correlation matrix.

[0103] Call the correlation strength list, filter the matrix elements whose correlation strength is higher than the set threshold within the time period, and output the task environment correlation matrix by accumulating and integrating the correlation strength list row by row and column by column;

[0104] The correlation strength data of each time period is screened. First, a correlation strength threshold is set, and all elements in the correlation list are judged one by one. The matrix elements with correlation strength greater than the threshold are screened out, and their position index in the time period is recorded. Then, the matrix elements that meet the conditions are aggregated in rows and columns, and the screened correlation strength values ​​are accumulated in the order of rows and columns to form the row cumulative data and column cumulative data of the matrix. Then, the row cumulative data and the column cumulative data are further merged to generate a high correlation data set between the task data and the disturbance factor in the time series. Finally, the task environment correlation matrix is ​​output through the segmented sorting and reorganization of the time period data as the overall correlation result of the task data and the disturbance factor.

[0105] See also Figure 4 , the specific steps for obtaining the task environment matching table are:

[0106] Based on the task-environment correlation matrix, filter the disturbance factors in the task that are correlated with the environment, identify the impact ratio of each disturbance factor on task execution, rank the disturbance factors by weight, and obtain the disturbance factor weight ranking table;

[0107] Based on the real environment data of the mission in the flight experiment, by setting the threshold of the specific correlation degree, multiple variables of the mission environment, such as wind speed, air pressure change, temperature and humidity gradient and other disturbance factors, are compared item by item to quantify the degree of interference with the completion of the flight mission. For example, in the flight experiment, assuming that the impact of wind speed disturbance on the route deviation is greater than 10%, the disturbance factor is marked as an important interference parameter, and the influence range and intensity of the disturbance factor are analyzed one by one. By observing the changes in the mission completion degree, such as the flight path stability, the target point arrival time deviation and other key indicators, the influence ratio of each disturbance factor on the mission completion is judged. Based on the results of the influence ratio, the weight allocation and ranking of the disturbance factors are carried out according to their significance. The weight ranking table of the disturbance factors is generated by using the summary results of the real flight data, and the weight ranking results of the disturbance factors are imported into the task association model, laying the foundation for the subsequent grouping and time series analysis.

[0108] Based on the disturbance factor weight ranking table, the top-ranked disturbance factors are grouped one by one according to the time series, and the time position of each disturbance factor is divided. The weight accumulation of the disturbance factors in the time node is calculated and mapped to the task cycle time point to generate the disturbance factor time distribution table;

[0109] Combined with the interaction between the task sequence and environmental disturbances in the flight test data, the impact time of each disturbance factor is combined with the weight change trend, and the wind speed disturbance changes in different periods of the flight simulation mission cycle are observed. The disturbance factors are grouped into early, middle and late stages according to the weight value. For example, the wind speed may have the greatest impact on the route within the first 10 minutes of the flight, while the pressure change significantly interferes with the altitude maintenance in the second half of the flight mission. The two need to be grouped into different time series. Based on this, the weighted cumulative value of the disturbance factor in each time node is calculated, the cumulative disturbance intensity at each time point is summarized, and it is mapped to the specific time point in the mission cycle to generate a time distribution table of the disturbance factor. This time distribution table not only reflects the disturbance law during the execution of the mission, but also provides a data-based theoretical basis for the design of subsequent control strategies for the mission.

[0110] Based on the disturbance factor time distribution table, the task cycle is divided into multiple stages, the task stages are matched with the disturbance factor time distribution, the weighted sum of the disturbance factor weights within the stage is calculated, the task cycle disturbance distribution and the stage correlation are integrated, and the task environment matching table is generated;

[0111] Combined with the division criteria of the mission cycle in the flight test data, such as the three stages of take-off, cruising and landing, the key objectives of each mission stage are clarified. By analyzing the time distribution table, the disturbance factors and their cumulative weights in different stages are matched one by one, and the main interference factors and their influence degree in the stage are determined. Taking the cruise stage of flight simulation as an example, if it is found that the cumulative weight of wind speed disturbance and air pressure change in this stage exceeds 70%, it is necessary to focus on the interference effect of these two types of disturbance factors in the cruise stage. When calculating the weighted sum of the disturbance factor weights in the stage, it is necessary to linearly superimpose each disturbance item according to the actual influence ratio of the disturbance factor, summarize the total interference value of each stage, integrate the mission cycle disturbance distribution and the stage correlation relationship, and generate a mission environment matching table. The matching table can intuitively reflect the key disturbances of each mission stage and their position and intensity relationship in the mission cycle, and provide data support for flight mission interference control and stage optimization.

[0112] See also Figure 5 , the specific steps for obtaining the flight trajectory distribution results are:

[0113] According to the task environment matching table, the coordinate parameters of the three-dimensional trajectory points in the test data are extracted, and the displacement change of the trajectory points in the three-dimensional space and the distribution characteristics of the disturbance area are analyzed through the coordinate parameters. The concentrated distribution of the disturbance range is judged by using the position parameters between the trajectory points, and the matching results of the trajectory point position change and the disturbance area are generated;

[0114] The initial coordinate value and spatial distribution of each trajectory point are analyzed step by step, the spatial position information between adjacent trajectory points is called, the spatial displacement change between trajectory points is obtained through differential operation, and the change trend of trajectory points in three-dimensional space is judged based on the timestamp parameters of each trajectory point. The cumulative distribution of displacement of trajectory points is further analyzed. By comparing the change rules of different trajectory points on the time axis, the displacement data is matched with the disturbance area parameters, the concentrated distribution range of the disturbance area is extracted, and the correspondence analysis of trajectory points and disturbance area parameters is performed to calculate the concentration degree of trajectory points in each disturbance area. Combined with the three-dimensional spatial position information, the matching results of trajectory point position change and disturbance area are generated to provide the required input for subsequent correction and modeling.

[0115] Based on the matching results of the trajectory point position change and the disturbance area, the position offset of the trajectory point in the disturbance area is analyzed, the disturbance influence parameters and position deviation are called, and the correction coefficient and nonlinear weight parameters are adjusted, and the formula is used:

[0116]

[0117] Calculate the corrected trajectory point positions and obtain the corrected trajectory position distribution results;

[0118] in, represents the corrected trajectory point position, X represents the original trajectory point position, δ is the position correction coefficient, D represents the distance from the trajectory point to the center of the disturbance area, ΔX represents the position deviation, ΔR represents the disturbance influence parameter, and γ is the nonlinear weight parameter, which controls the correlation between the correction range and the position deviation;

[0119] The benefit of the formula is that it introduces position deviation and disturbance influence parameters, and adjusts the correction range through the weight γ, thereby achieving accurate correction of the trajectory point;

[0120] Given the original trajectory point position X = 10.5, the distance from the trajectory point to the center of the disturbance area D = 2.0, the position deviation |ΔX| = 0.5, the disturbance influence parameter ΔR = 4.0, the correction coefficient δ = 1.2, and the weight parameter γ = 2, substitute the parameters into the formula:

[0121]

[0122] The result shows that the corrected trajectory point position is 12.375. By calculating the correction formula, the position adjustment of the trajectory point in the disturbance area is achieved, and the corrected trajectory point distribution data required for subsequent modeling is further provided.

[0123] Based on the position distribution results after trajectory correction, call the disturbance area parameters and the target area parameters of the mission environment matching table, analyze the distribution density and concentration of the corrected trajectory points in the target area, and judge the coverage and range of the trajectory distribution area by statistically analyzing the distribution characteristics of the trajectory points to generate the flight trajectory distribution results;

[0124] Firstly, the position data after trajectory correction are classified and analyzed, and each trajectory point is spatially divided according to the position distribution. The coordinate density parameters of each corrected trajectory point in three-dimensional space are calculated. For each trajectory point, its spatial concentration is judged by the distribution law of adjacent points. The distribution boundary and coverage of the target area are further extracted. The deviation between the density parameters of the trajectory points and the distribution boundary of the target area is compared. The spatial attribution of the trajectory points is corrected using the disturbance area parameters. The distribution characteristics of the judgment area are accumulated, and the distribution density of the trajectory points in the target area is counted one by one. The distribution of trajectory points and the coverage of the target area are evaluated, and the data support required for trajectory modeling is provided.

[0125] See also Figure 6 ,The specific steps for obtaining the flight trajectory simulation model are:

[0126] Based on the flight trajectory distribution results, the coordinates and time series of the trajectory points in the disturbance-intensive area are extracted, the trajectory points are grouped and counted according to the coordinate spacing, the coordinate value and time parameter density are called, the trajectory points in the high-density group are annotated, and a classification and annotation table of trajectory points in the disturbance-intensive area is generated;

[0127] Based on actual flight data, the disturbance characteristics of the trajectory-dense areas in the flight mission are identified, the collected flight trajectory point data are screened, and the spatial distribution and time series of the trajectory point coordinates are used as the benchmark to lock the areas with higher disturbance intensity. For example, by observing the spatial aggregation degree of trajectory points, it is determined that the coordinate spacing of trajectory points in a certain area is less than a certain threshold, and the trajectory points are classified and grouped according to the continuity on the time axis, and the density changes of trajectory points in the time period are marked. After completing the group statistics, the distribution characteristics of the trajectory points are annotated by combining the coordinates and time parameter density of each group of trajectory points. The trajectory points in the disturbance-dense area are classified in an annotated manner, and finally a classification and annotation table of trajectory points in the disturbance-dense area is formed. This table directly reflects the distribution pattern of trajectory points in the flight simulation mission, which is helpful to analyze the interference mechanism of the high-density disturbance area on the flight trajectory.

[0128] Based on the classification and annotation table of trajectory points in the disturbance-intensive area, the coordinate data and time nodes of the annotated trajectory points are extracted, the disturbance amplitude data are matched with the trajectory points, the coordinate and time series parameters are used to perform linear interpolation on the disturbance amplitude between the trajectory points, and a dynamic trajectory point distribution table of the disturbance data is constructed;

[0129] According to the mission timeline of the flight simulation test, the spatial coordinates and time parameters of the trajectory points are extracted in segments, and matched in combination with the disturbance amplitude data. The position of each trajectory point in the time series is observed and associated with its corresponding disturbance amplitude data, and the mark matching is performed point by point to ensure the accurate correspondence between the disturbance amplitude and the trajectory point coordinates and time series. By calling the time series and coordinate value parameters of the trajectory points, the disturbance amplitude between the trajectory points is interpolated to construct a dynamic trajectory distribution model of the disturbance data. For example, the trajectory point data between two consecutive time nodes is selected, and the disturbance amplitude change between the two points is completed by linear interpolation to generate a dynamic trajectory point distribution table of the disturbance data. This table can reflect the dynamic changes in the disturbance intensity of the trajectory points in the flight mission, and provide key data support for trajectory analysis and model calibration.

[0130] Based on the dynamic trajectory point distribution table of disturbance data, the coordinate sequence of continuous dynamic trajectory points is connected, the trajectory curve is reorganized through the time node sequence, the curve interval and direction change between trajectory points are adjusted, the dynamic trend of the curve is calibrated, and the flight trajectory simulation model is generated;

[0131] Based on the time series of trajectory points, the trajectory curves are connected point by point and reorganized to show the dynamic change trend of the flight trajectory. The spatial coordinates of the trajectory points are linearly connected, and the overall shape of the trajectory curve is constructed in the order of time nodes. In the connection process, it is necessary to combine the curve interval and direction changes between the trajectory points, and make adjustments for the areas that deviate greatly from the standard trajectory to ensure the smoothness and continuity of the trajectory curve, calibrate the dynamic trend of each curve segment, and optimize the connection method by analyzing the distribution law and change direction of the trajectory points segment by segment to make it more accurately reflect the actual distribution characteristics of the flight trajectory. Finally, a flight trajectory simulation model is generated, which can visualize the impact of disturbances in the test data and provide a dynamic simulation basis for flight trajectory optimization.

[0132] See also Figure 7 ,The specific steps for obtaining the flight control solution driven by test data are:

[0133] Based on the flight trajectory simulation model, the attitude point parameters in the simulation trajectory are extracted, and the three-dimensional coordinates and time series distribution of the attitude points are analyzed point by point. The attitude point parameters and trajectory control variables are called to calculate the change rate on the time axis. By comparing the change amount of each attitude point between consecutive time nodes, the time series change trend of the attitude point parameters is obtained.

[0134] By analyzing the three-dimensional coordinate data of each posture point point by point, and associating the coordinate value with the time series, calling the spatial position change of the posture point between consecutive time nodes, calculating the displacement change of each posture point in the time dimension, and performing differential calculation on the change in combination with the time interval, the displacement rate and spatial trajectory parameters between the posture points are obtained. Then, the posture point parameters and time nodes are segmented, and the displacement rate and change trend are cumulatively calculated according to the time series. The time response deviation of adjacent posture points is compared through the cumulative results to determine the change trend and cumulative characteristics of the posture points at different time nodes. The offset characteristics of the trajectory point parameters distributed over time are further analyzed, and the time series change characteristics and the spatial change parameters of the posture points are comprehensively processed to extract the time series change law and cumulative change trend of the posture points. Finally, the time series change trend of the posture point parameters is generated to provide data support for the subsequent optimization of trajectory control variables.

[0135] According to the time series change trend of the attitude point parameters, the trajectory control variable parameters are called, and the response amplitude of the control variable and the attitude point change rate are adjusted through weight distribution optimization. The formula is:

[0136]

[0137] Calculate the time series weight value of the control variable and generate the optimized control variable and attitude point time series weight distribution result;

[0138] Among them, W k represents the time series weight value of the control variable, P k represents the initial value of the control variable, Q k Indicates the rate of change of attitude points, T k represents the time threshold parameter, C k represents the trajectory disturbance correlation coefficient, α represents the time series distribution weight coefficient;

[0139] The benefit of the formula is that by introducing the time threshold parameter and the disturbance correlation coefficient, the response amplitude of the control variable at the time node and the change rate of the attitude point are comprehensively considered, and the time series distribution logic of the weight is optimized;

[0140] Parameter acquisition and value assignment:

[0141] P k : Initial value of control variable, obtained by extracting control input data from flight test, set P k =5.0;

[0142] Q k : The rate of change of the attitude point is calculated by the spatial coordinate difference and time interval of adjacent time nodes, and Q k =7.0;

[0143] T k : Time threshold parameter, defined as the reference time for response control, taken as T k =4.0;

[0144] C k : Trajectory disturbance correlation coefficient, obtained through simulation calculation of disturbance environment and analysis of feedback parameters, C k =2.0;

[0145] α: time series weight coefficient, obtained by coupling analysis of time node distribution and control parameters, set to α = 3.0;

[0146] Substitute into the formula to calculate:

[0147] W k =(5.0+|7.0-4.0|)·2.0 3.0

[0148] W k =(5.0+3.0)·8.0

[0149] W k =8.0·8.0=64.0

[0150] This result shows that the time series weight W kIt is 64.0, which represents the optimized weight distribution of the control variables at the current time node. The optimized weight distribution result will be used to match the time series relationship between the posture point parameters and the control variables.

[0151] Based on the optimized control variables and attitude point time series weight distribution results, match the disturbance environment parameters, analyze the matching degree between the control variables and the disturbance environment, optimize the flight control logic in combination with the attitude point time series distribution, define the flight instructions and adjust the response amplitude of the control variables at the time nodes, and generate the flight control plan driven by the test data;

[0152] The disturbance environment parameters are called to compare and analyze the time response data of the attitude points, and the response amplitude of the control variable parameters at the time nodes is gradually adjusted. The relationship between the control amplitude of each time node and the change of the attitude point is calculated through segmented time domain processing. The matching degree between the disturbance environment and the control variable is optimized by using the time series distribution characteristics. The time domain distribution characteristics of the time node parameters are analyzed one by one, and the optimization parameters of the control variables are mapped with the time domain data of the attitude points. The weight response amplitude at each time node is compared, and the control variable response parameters in the time series distribution are gradually adjusted. The response logic of the flight command is defined, and the flight control scheme driven by the test data is finally generated by optimizing the matching attitude point time series distribution and the disturbance environment parameters. The flight control scheme refers to a set of systematic strategies that aims to adapt to environmental disturbances by dynamically adjusting the attitude and speed of the aircraft to ensure flight stability and efficiency. This scheme will include the preset flight path, attitude adjustment, speed control and necessary emergency response instructions of the aircraft. By optimizing the matching attitude point time series distribution and the disturbance environment parameters, a flight control scheme driven by the test data is finally generated, so that the aircraft can maintain optimal flight performance under different environmental conditions.

[0153] The flight simulation method based on test data is performed based on the above-mentioned flight simulation system based on test data, and includes the following steps:

[0154] S1: Based on the basic data of the aircraft test, extract the power parameters, attitude parameters and test environment variables, remove the mutation data, perform time normalization on the attitude parameters, remove the abnormal fluctuation items and interference values, and generate the calibration flight parameter set;

[0155] S2: Based on the calibration flight parameter set, extract the aircraft mission data and test environment disturbance factors, analyze the correlation coefficient and variation range, sort the disturbance factors by time point, match the mission phase data, and generate the mission environment matching table;

[0156] S3: Based on the task environment matching table, extract the three-dimensional position change parameters of the trajectory points, group and classify them into disturbance areas, analyze the trend and density distribution of trajectory points in the disturbance-intensive areas, match the disturbance weights of the time periods, and generate the flight trajectory distribution results;

[0157] S4: Based on the flight trajectory distribution results, the time series and attitude parameters of the trajectory points in the disturbance-intensive area are extracted, the trajectory point positions and attitude parameters are fitted in segments, the disturbance factor weights are matched, and the flight trajectory simulation model is generated;

[0158] S5: Based on the flight trajectory simulation model, extract the attitude point parameters and trajectory control variables, numerically analyze the attitude point time series in segments, match the disturbance area time points, optimize and integrate the control variables and attitude point parameters, and generate a flight control plan driven by experimental data.

[0159] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A flight simulation system based on test data, characterized in that: The system comprises: The aircraft parameter calibration module extracts power parameters, attitude parameters, and test environment variables based on the basic data of aircraft tests, performs interpolation correction and flight phase parameter normalization, adjusts the change trend of test environment variables, and generates a calibration flight parameter set; The mission environment association analysis module extracts the aircraft mission data and the test environment disturbance factors based on the calibration flight parameter set, analyzes the change range and correlation strength of the mission data and the disturbance factors in the differentiated time periods, generates a mission environment association matrix, screens the associated disturbance factors and sorts them by time series weight, and maps the disturbance distribution to the mission phase to generate a mission environment matching table; The flight trajectory modeling module analyzes the three-dimensional position change characteristics of the trajectory points and the disturbance distribution area in the test data based on the mission environment matching table, generates a flight trajectory distribution result, classifies and labels the trajectory points in the disturbance-intensive area, and simulates the dynamic flight trajectory curve in combination with the disturbance data to generate a flight trajectory simulation model; Based on the flight trajectory simulation model, the control strategy optimization module extracts the attitude point parameters and trajectory control variables in the simulated trajectory, optimizes the control variables and the attitude point time series weight distribution, matches the disturbance environment, adjusts the flight control logic, defines the flight instructions, and generates a flight control plan driven by test data.

2. The flight simulation system based on test data according to claim 1, characterized in that: The steps for obtaining the calibration flight parameter set are specifically as follows: Based on the basic data of the aircraft test, the power parameters, attitude parameters and test environment variables are called, the change trend of the test environment variables is normalized in sections, the change rate in each section is calculated, and the weighted change rate set of attitude parameters is obtained by mapping the change rate with the attitude parameters; Call the weighted change rate set of the posture parameters, set the weight parameters of the segments and associate them with the weighted change rate, using the formula: Generate normalized parameter results for flight phase; Among them, P norm represents the normalized value of the flight phase parameter, V i is the weighted change rate of the posture parameters of the i-th segment, W i is the weight parameter, n represents the total number of segments; Based on the normalized parameter results of the flight phase, the segmented normalized change rate set of the test environment variables is called to dynamically correct the normalized parameters. By comparing the normalized differences of the parameters in each phase, adjusting the weight abnormal items, and eliminating data with large deviations, a calibrated flight parameter set is obtained.

3. The flight simulation system based on test data according to claim 2, characterized in that: The steps for obtaining the task environment association matrix are specifically as follows: Based on the calibration flight parameter set, the aircraft mission data and the test environment disturbance factor are called, the variation range of the mission data and the disturbance factor is extracted in segments according to the time series, each variation value is normalized, and a preliminary variation range set of the mission data and the disturbance factor is generated by preliminary weighted operation of the normalized data; The preliminary variation range set of the task data and the disturbance factor is called, and the correlation strength between the task data and the disturbance factor within the time period is calculated using the formula: Generate a list of strengths of association; Among them, R represents the correlation strength value between task data and disturbance factor, T j is the task data variation range in the jth time period, E j is the range of disturbance factor variation in the jth time period, F j is the weight coefficient of the jth time period, C is the smoothing adjustment parameter, and m represents the total number of time periods; The association strength list is called to filter the matrix elements whose association strength is higher than a set threshold within a time period, and the task environment association matrix is ​​output by accumulating and integrating the association strength list row by row and column by column.

4. The flight simulation system based on test data according to claim 3, characterized in that: The steps for obtaining the task environment matching table are specifically as follows: Based on the task-environment correlation matrix, the disturbance factors in the task that are correlated with the environment are screened, the influence ratio of each disturbance factor on the task execution is identified, the disturbance factors are weighted and ranked, and a disturbance factor weight ranking table is obtained; Based on the disturbance factor weight ranking table, the top-ranked disturbance factors are grouped one by one according to the time series, the time position of each disturbance factor is divided, the weight accumulation of the disturbance factors in the time node is calculated and mapped to the task cycle time point, and the disturbance factor time distribution table is generated; Based on the disturbance factor time distribution table, the task cycle is divided into multiple stages, the task stages are matched with the disturbance factor time distribution, the weighted sum of the disturbance factor weights within the stage is calculated, the task cycle disturbance distribution and the stage association are integrated, and a task environment matching table is generated.

5. The flight simulation system based on test data according to claim 4, characterized in that: The steps for obtaining the flight trajectory distribution result are specifically as follows: According to the task environment matching table, the coordinate parameters of the three-dimensional trajectory points in the test data are extracted, the displacement change of the trajectory points in the three-dimensional space and the distribution characteristics of the disturbance area are analyzed by the coordinate parameters, the concentrated distribution of the disturbance range is judged by the position parameters between the trajectory points, and the matching results of the trajectory point position change and the disturbance area are generated; Based on the matching results of the trajectory point position change and the disturbance area, the position offset of the trajectory point in the disturbance area is analyzed, the disturbance influence parameter and the position deviation are called, and the correction coefficient and the nonlinear weight parameter are adjusted, and the formula is adopted: Calculate the corrected trajectory point positions and obtain the corrected trajectory position distribution results; in, represents the corrected trajectory point position, X represents the original trajectory point position, δ is the position correction coefficient, D represents the distance from the trajectory point to the center of the disturbance area, ΔX represents the position deviation, ΔR represents the disturbance influence parameter, and γ is the nonlinear weight parameter, which controls the correlation between the correction range and the position deviation; Based on the position distribution result after trajectory correction, the disturbance area parameters and the target area parameters of the mission environment matching table are called, the distribution density and concentration of the corrected trajectory points in the target area are analyzed, and the coverage and range of the trajectory distribution area are judged by statistically analyzing the distribution characteristics of the trajectory points to generate the flight trajectory distribution result.

6. The flight simulation system based on test data according to claim 5, characterized in that: The steps for obtaining the flight trajectory simulation model are specifically as follows: Based on the flight trajectory distribution results, the coordinates and time series of the trajectory points in the disturbance-intensive area are extracted, the trajectory points are grouped and counted according to the coordinate spacing, the coordinate value and time parameter density are called, the trajectory points of the high-density group are marked, and a classification and marking table of trajectory points in the disturbance-intensive area is generated; Based on the disturbance intensive area trajectory point classification and annotation table, the coordinate data and time nodes of the annotated trajectory points are extracted, the disturbance amplitude data are matched with the trajectory points, the coordinate and time series parameters are called to linearly interpolate the disturbance amplitudes between the trajectory points, and a dynamic trajectory point distribution table of the disturbance data is constructed; Based on the dynamic trajectory point distribution table of the disturbance data, the coordinate sequence of continuous dynamic trajectory points is connected, the trajectory curve is reorganized through the time node sequence, the curve interval and direction change between trajectory points are adjusted, the dynamic trend of the curve is calibrated, and a flight trajectory simulation model is generated.

7. The flight simulation system based on test data according to claim 6, characterized in that: The steps for obtaining the flight control scheme driven by the test data are specifically as follows: Based on the flight trajectory simulation model, the attitude point parameters in the simulation trajectory are extracted, and the three-dimensional coordinates and time series distribution of the attitude points are analyzed point by point, the attitude point parameters and trajectory control variables are called, and the change rate on the time axis is calculated. By comparing the change amount of each attitude point between consecutive time nodes, the time series change trend of the attitude point parameters is obtained; According to the time series change trend of the attitude point parameters, the trajectory control variable parameters are called, and the response amplitude of the control variable and the attitude point change rate are adjusted through weight distribution optimization, using the formula: Calculate the time series weight value of the control variable and generate the optimized control variable and attitude point time series weight distribution result; Among them, W k represents the time series weight value of the control variable, P k represents the initial value of the control variable, Q k Indicates the rate of change of attitude points, T k represents the time threshold parameter, C k represents the trajectory disturbance correlation coefficient, α represents the time series distribution weight coefficient; Based on the optimized control variables and attitude point time series weight distribution results, the disturbance environment parameters are matched, the matching degree of the control variables and the disturbance environment is analyzed, the flight control logic is optimized in combination with the attitude point time series distribution, the flight instructions are defined and the response amplitude of the control variables at the time nodes is adjusted, and the flight control scheme driven by the test data is generated.

8. A flight simulation method based on test data, characterized in that: The flight simulation system based on test data according to any one of claims 1 to 7 is implemented, comprising the following steps: Based on the basic data of the aircraft test, the power parameters, attitude parameters and test environment variables are extracted, the mutation data are eliminated, the attitude parameters are time-normalized, the fluctuation abnormal items and interference values ​​are eliminated, and the calibration flight parameter set is generated; Based on the calibration flight parameter set, extracting aircraft mission data and test environment disturbance factors, analyzing correlation coefficients and variation ranges, sorting disturbance factors by time points, matching mission phase data, and generating a mission environment matching table; Based on the task environment matching table, the three-dimensional position change parameters of the trajectory points are extracted, the disturbance areas are classified and grouped, the trend and density distribution of the trajectory points in the disturbance-intensive areas are analyzed, the disturbance weights of the time periods are matched, and the flight trajectory distribution results are generated; Based on the flight trajectory distribution results, the time series and attitude parameters of the trajectory points in the disturbance-intensive area are extracted, the trajectory point positions and attitude parameters are fitted in sections, the disturbance factor weights are matched, and a flight trajectory simulation model is generated; Based on the flight trajectory simulation model, attitude point parameters and trajectory control variables are extracted, the attitude point time series is numerically segmented and analyzed, the disturbance area time points are matched, the control variables and attitude point parameters are optimized and integrated, and a flight control scheme driven by experimental data is generated.

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