Flight commenting system and evaluation method based on flight parameter data

Abstract

The invention belongs to the technical field of data processing, and discloses a flight commenting system and evaluation method based on flight parameter data, and the system comprises a training task module which is used for making a flight plan, discriminating a flight subject corresponding to the flight plan, and obtaining a corresponding flight action list based on the flight subject; according to the method, the problems of flight playback jittering and the like are solved by applying flight parameter data noise elimination, outlier elimination and defect data interpolation fitting methods, and the three-dimensional flight playback effect is effectively improved; a dynamic smooth interpolation method based on a rendering frame rate and a flight parameter time scale alignment method based on Beijing time are applied to solve the problem of alignment of flight parameter data and time scales of multiple aircrafts, and space-time unification during playback of the multiple aircrafts is realized; according to the method, trend analysis can be carried out on the control deviation, improved measures are given accordingly, correction of flight actions is facilitated, the rendering effect can be optimized by providing a three-dimensional scene configuration interface and configuring satellite images and elevation data, and the immersion of flight reproduction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a flight review system and evaluation method based on flight parameter data. Background Art

[0002] When reviewing flight training, the flight data have the following problems: During flight training, the flight data is easily affected by various electromagnetic interferences during collection, transmission and storage, which leads to noise and even wild value problems in the flight data. In addition, when playing back multiple aircraft, the flight data time drifts and the Beijing time is discontinuous, which makes it difficult to unify the time base.

[0003] In view of this, the present invention proposes a flight review system and evaluation method based on flight parameter data to solve the above problems. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a flight review system based on flight parameter data, comprising:

[0005] A training task module is used to formulate a flight plan and determine the flight subject corresponding to the flight plan, and obtain the corresponding flight action list based on the flight subject;

[0006] The data analysis module is connected to the training task module and is used to collect and fuse the multi-source raw data of the flight action list corresponding to the flight action, thereby obtaining the flight parameter data, and analyzing the flight parameter data to obtain the analysis results;

[0007] The review module is connected to the data analysis module and is used to judge the results and conduct a visual review of the analysis results according to the established evaluation criteria and standard operations, thereby obtaining subject evaluation and manipulation deviations;

[0008] The evaluation module is connected to the review module and is used to obtain the historical results of standard operations and perform trend analysis on the manipulation deviations based on the historical results to find the problems;

[0009] The review module is connected to the evaluation module, and is used to obtain improvement measures for flight actions based on the problems found, and to feed back the problems and improvement measures to the flight plan.

[0010] Preferably, the method of formulating a flight plan, determining the flight subject corresponding to the flight plan, and obtaining the corresponding flight action list based on the flight subject includes:

[0011] Develop flight plans and establish subject criteria library;

[0012] The flight subjects in the flight plan are judged according to the subject judgment library, so as to obtain the flight subjects in the flight plan;

[0013] Summarize the flight subjects in the flight plan and obtain a flight action list.

[0014] Preferably, the method of collecting multi-source original data corresponding to the flight action list of the flight action and fusing them to obtain the flight parameter data, and analyzing the flight parameter data to obtain the analysis results includes:

[0015] Collect multi-source original data of the aircraft, establish a source database, and store the multi-source original data in the source database;

[0016] The multi-source raw data are fused to obtain flight parameter data, and then the flight parameter data are dynamically smoothed and time-scale aligned, wherein the fusion process includes noise elimination and outlier removal;

[0017] Establish a formula criterion library, calculate the flight parameter data based on the formula criterion library, and then obtain the analysis results;

[0018] A flight day schedule database is established, and the flight plan maker manually enters the flight plan into the flight day schedule database, and stores the analysis results in the flight day schedule database.

[0019] Preferably, the noise reduction method includes:

[0020] Taking any continuous original data among the multi-source original data as the first original input signal S1, decomposing the first original input signal S1, and then obtaining a first decomposition low-pass LO_D1 and a first decomposition high-pass HI_D1;

[0021] Perform binary downsampling on the first decomposition low-pass LO_D1 to obtain a first low-frequency component A1, and perform binary downsampling on the first decomposition high-pass HI_D1 to obtain a first high-frequency component D1;

[0022] Decompose the first low-frequency component A1 to obtain a second decomposition low-pass component LO_D2 and a second decomposition high-pass component HI_D2;

[0023] Perform binary downsampling on the second decomposition low-pass LO_D2 to obtain a second low-frequency component A2, and perform binary downsampling on the second decomposition high-pass HI_D2 to obtain a second high-frequency component D2;

[0024] Decompose the second low-frequency component A2 to obtain a third decomposition low-pass filter LO_D3 and a third decomposition high-pass filter HI_D3;

[0025] Perform binary downsampling on the third decomposition low-pass LO_D3 to obtain a third low-frequency component A3, and perform binary downsampling on the third decomposition high-pass HI_D3 to obtain a third high-frequency component D3;

[0026] Performing threshold processing on the third low-frequency component A3 to obtain a reconstructed third low-frequency component A'3, and performing threshold processing on the third high-frequency component D3 to obtain a reconstructed third high-frequency component D'3;

[0027] Perform binary upsampling on the third low-frequency component A`3 to obtain a third reconstructed low-pass LO_R3, perform binary upsampling on the third high-frequency component D`3 to obtain a third reconstructed high-pass HI_R3, reconstruct the third reconstructed low-pass LO_R3 and the third reconstructed high-pass HI_R3, and then obtain a reconstructed second low-frequency component A2`;

[0028] Perform threshold processing on the second high-frequency component D2 to obtain a reconstructed second high-frequency component D2';

[0029] Perform binary upsampling on the reconstructed second high-frequency component D2` to obtain a second reconstructed high-pass HI_R2, perform binary upsampling on the reconstructed second low-frequency component A2` to obtain a second reconstructed low-pass LO_R2, reconstruct the second reconstructed high-pass HI_R2 and the second reconstructed low-pass LO_R2, and then obtain a reconstructed first low-frequency component A1`;

[0030] Perform threshold processing on the first high-frequency component D1 to obtain a reconstructed first high-frequency component D1';

[0031] The reconstructed first high-frequency component D1` is binary upsampled to obtain a first reconstructed high-pass HI_R1, and the reconstructed first low-frequency component A1` is binary upsampled to obtain a first reconstructed low-pass LO_R1. The first reconstructed high-pass HI_R1 and the first reconstructed low-pass LO_R1 are combined to obtain a denoised output signal.

[0032] Preferably, the method of removing outliers includes:

[0033] Taking any continuous original data among the multi-source original data as the second original input signal S2, decomposing the second original input signal, and then obtaining a fourth decomposition low-pass LO_D4 and a fourth decomposition high-pass HI_D4;

[0034] Perform binary downsampling on the fourth decomposition low-pass filter LO_D4 to obtain a fourth low-frequency component A4, and perform binary downsampling on the fourth decomposition high-pass filter HI_D4 to obtain a fourth high-frequency component D4;

[0035] The fourth high-frequency component D4 is searched for the modulus maximum value, and binary downsampling is performed according to the mutation point range of the Laida criterion, that is, the data corresponding to the original data is cleared to zero;

[0036] The missing data of the second original input signal S2 is fitted to output a signal.

[0037] Preferably, the dynamic smoothing method includes:

[0038] To start, initialize the rendering engine, clear the playback timer dsimtine=0 and the playback cursor Index=0;

[0039] Then determine whether the frame loop is finished;

[0040] If yes, then end dynamic smoothing;

[0041] If not, calculate the frame cycle time deltTime and the frame playback step Δt=scalxdeltTime, then accumulate the playback time dsimtime=dsimtime+Δt, calculate the playback cursor position Index=(int)dsimt1me, and dynamically calculate the smooth difference point based on the cursor Index and the playback step Δt, and finally update the helicopter attitude and position until the frame cycle ends;

[0042] The time scale alignment method includes:

[0043] To begin with, obtain the flight parameter time stamp in each flight parameter data;

[0044] Initialize parameters and traverse the satellite reception status bits;

[0045] Determine whether it is 0;

[0046] If yes, re-traverse the satellite receiving status bits;

[0047] If not, mark the power-on time t1 of the integrated navigation system, continue to traverse the satellite reception status bit from time t1, and then determine whether it is 1;

[0048] If not, continue to traverse the satellite receiving status bits from time t1;

[0049] If yes, mark the satellite alignment time t2 of the integrated navigation system, and calculate the difference between Beijing time and flight reference time at time t2;

[0050] After traversing all flight parameter times and calibrating them, the alignment of the flight parameter time scales can be completed.

[0051] Preferably, the method of performing grade determination and visual review of the analysis results according to the established evaluation criteria and standard operations, and then obtaining the subject evaluation and manipulation deviation, includes:

[0052] Design a criterion editor based on dynamic compilation technology, formulate the evaluation criteria for each flight subject in the criterion editor, make performance judgments based on the evaluation criteria and analysis results, and then obtain the subject evaluation for each subject;

[0053] Divide flight training into flight phases, formulate standard operations for flight actions corresponding to each flight phase, and store the standard operations in the standard operation criterion library;

[0054] Compare the standard operation with the flight operation in the visual replay to obtain the operational deviation of the flight action;

[0055] Among them, the methods of visual review include:

[0056] Build 3D scenes based on Qt and OSG&OSGEarth 3D engines;

[0057] Establish a configuration interface for the 3D scene, and use the configuration interface to configure satellite images and elevation data to render the 3D scene;

[0058] Perform technical optimization on rendered 3D scenes;

[0059] The flight parameter data is used to drive the helicopter model to reproduce the flight in a three-dimensional scene and synchronize the flight parameter data to obtain a visual review. The flight parameter data includes driving instruments, cabin sounds, multi-viewing angles, all-round observation of flight trajectory, projection, and attitude.

[0060] Preferably, the historical results of standard operations are obtained, and trend analysis of manipulation deviations is performed based on the historical results, that is, the method of finding the problems includes:

[0061] Obtain historical results of standard operations and store the historical results in a preset standard operation criterion library;

[0062] Calculate the control deviations corresponding to historical performance and current flight parameter data, and plot multiple control deviations into a trend chart in chronological order;

[0063] Find the flight parameter data with a control deviation greater than e% in the trend chart as abnormal points, and take the flight actions corresponding to the abnormal points as the problems to be found.

[0064] Preferably, the method of obtaining improvement measures for the flight action according to the found problems and feeding back the found problems and improvement measures to the flight plan includes:

[0065] Add the flight actions corresponding to the found problems to the flight action list;

[0066] Obtain the abnormal points of the flight action corresponding to the found problem, and use the control deviation between the flight parameter data corresponding to the abnormal point and the flight parameter data of the standard operation as an improvement measure;

[0067] Feedback improvement measures to the flight plan so that the flight actions corresponding to the problem can be improved next time.

[0068] A flight evaluation method based on flight parameter data comprises the following steps:

[0069] Step 1: Formulate a flight plan and identify the flight subjects corresponding to the flight plan, and obtain the corresponding flight action list based on the flight subjects;

[0070] Step 2: Collect the multi-source original data corresponding to the flight action list and perform fusion processing to obtain the flight parameter data, and analyze the flight parameter data to obtain the analysis results;

[0071] Step 3: Perform grade judgment and visual review of the analysis results based on the established evaluation criteria and standard operations, and then obtain subject evaluation and manipulation deviations;

[0072] Step 4: Obtain the historical results of standard operations, and conduct trend analysis on the manipulation deviations based on the historical results to find the problems;

[0073] Step 5: Obtain improvement measures for flight actions based on the problems found, and feed the problems and improvement measures back to the flight plan.

[0074] The technical effects and advantages of the flight review system and evaluation method based on flight parameter data of the present invention are as follows:

[0075] 1. By applying flight parameter data noise elimination, outlier elimination and defective data interpolation fitting methods, the problem of flight playback jitter is solved, and the three-dimensional flight playback effect is effectively improved; the dynamic smoothing interpolation method based on rendering frame rate and the flight parameter time scale alignment method based on Beijing time are applied to solve the time scale alignment problem of multi-aircraft flight parameter data, and achieve time and space unification during multi-aircraft playback;

[0076] 2. By dividing the flight phases and establishing standard operations and evaluation criteria, each flight subject and the flight actions within the flight subject can be evaluated, so as to obtain the control deviation, and through the trend analysis of the control deviation, find out the abnormal points, and give improvement measures, which is conducive to the correction of flight actions;

[0077] 3. By providing a 3D scene configuration interface and configuring satellite images and elevation data, the rendering of high-precision 3D scenes of any scale range is achieved, and the rendering effect is optimized through dynamic loading, layered detail processing and other technologies, thereby enhancing the immersive feeling of flight reproduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1It is a structural schematic diagram of a flight review system based on flight parameter data of the present invention;

[0079] Figure 2 A schematic diagram of a flow chart of a flight evaluation method based on flight parameter data of the present invention;

[0080] Figure 3 It is a business process diagram of the present invention;

[0081] Figure 4 It is a flow chart of outlier elimination based on wavelet transform of the present invention;

[0082] Figure 5 It is a flow chart of denoising based on wavelet transform of the present invention;

[0083] Figure 6 It is a dynamic smooth interpolation flow chart of the present invention;

[0084] Figure 7 The present invention is a flow chart of the flight parameter time-scale alignment. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] Example 1

[0087] See also Figure 1 , Figures 3 to 7 As shown, the flight review system based on flight parameter data described in this embodiment includes:

[0088] A training task module is used to formulate a flight plan and determine the flight subject corresponding to the flight plan, and obtain the corresponding flight action list based on the flight subject;

[0089] The data analysis module is connected to the training task module and is used to collect and fuse the multi-source raw data of the flight action list corresponding to the flight action, thereby obtaining the flight parameter data, and analyzing the flight parameter data to obtain the analysis results;

[0090] The review module is connected to the data analysis module and is used to judge the results and conduct a visual review of the analysis results according to the established evaluation criteria and standard operations, thereby obtaining subject evaluation and manipulation deviations;

[0091] The evaluation module is connected to the review module and is used to obtain the historical results of standard operations and perform trend analysis on the manipulation deviations based on the historical results to find the problems;

[0092] The review module is connected to the evaluation module, and is used to obtain improvement measures for flight actions based on the problems found, and to feed back the problems and improvement measures to the flight plan.

[0093] Furthermore, the method of formulating a flight plan and determining the flight subject corresponding to the flight plan, and obtaining the corresponding flight action list based on the flight subject includes:

[0094] Develop flight plans and establish subject criteria library;

[0095] The flight subjects in the flight plan are judged according to the subject judgment library, so as to obtain the flight subjects in the flight plan;

[0096] Summarize the flight subjects in the flight plan and obtain a flight action list.

[0097] Furthermore, the multi-source original data corresponding to the flight action list of the flight action list is collected and fused to obtain the flight parameter data, and the flight parameter data is analyzed to obtain the analysis result, including:

[0098] Collect multi-source original data of the aircraft, establish a source database, and store the multi-source original data in the source database;

[0099] The multi-source raw data are fused to obtain flight parameter data, and then the flight parameter data are dynamically smoothed and time-scale aligned, wherein the fusion process includes noise elimination and outlier removal;

[0100] Establish a formula criterion library, calculate the flight parameter data based on the formula criterion library, and then obtain the analysis results;

[0101] A flight day schedule database is established, and the flight plan maker manually enters the flight plan into the flight day schedule database, and stores the analysis results in the flight day schedule database.

[0102] Furthermore, the denoising method based on wavelet transform includes:

[0103] Taking any continuous original data among the multi-source original data as the first original input signal S1, decomposing the first original input signal S1, and then obtaining a first decomposition low-pass LO_D1 and a first decomposition high-pass HI_D1;

[0104] Perform binary downsampling on the first decomposition low-pass LO_D1 to obtain a first low-frequency component A1, and perform binary downsampling on the first decomposition high-pass HI_D1 to obtain a first high-frequency component D1;

[0105] Decompose the first low-frequency component A1 to obtain a second decomposition low-pass component LO_D2 and a second decomposition high-pass component HI_D2;

[0106] Perform binary downsampling on the second decomposition low-pass LO_D2 to obtain a second low-frequency component A2, and perform binary downsampling on the second decomposition high-pass HI_D2 to obtain a second high-frequency component D2;

[0107] Decompose the second low-frequency component A2 to obtain a third decomposition low-pass filter LO_D3 and a third decomposition high-pass filter HI_D3;

[0108] Perform binary downsampling on the third decomposition low-pass LO_D3 to obtain a third low-frequency component A3, and perform binary downsampling on the third decomposition high-pass HI_D3 to obtain a third high-frequency component D3;

[0109] Performing threshold processing on the third low-frequency component A3 to obtain a reconstructed third low-frequency component A'3, and performing threshold processing on the third high-frequency component D3 to obtain a reconstructed third high-frequency component D'3;

[0110] Perform binary upsampling on the third low-frequency component A`3 to obtain a third reconstructed low-pass LO_R3, perform binary upsampling on the third high-frequency component D`3 to obtain a third reconstructed high-pass HI_R3, reconstruct the third reconstructed low-pass LO_R3 and the third reconstructed high-pass HI_R3, and then obtain a reconstructed second low-frequency component A2`;

[0111] Perform threshold processing on the second high-frequency component D2 to obtain a reconstructed second high-frequency component D2';

[0112] Perform binary upsampling on the reconstructed second high-frequency component D2` to obtain a second reconstructed high-pass HI_R2, perform binary upsampling on the reconstructed second low-frequency component A2` to obtain a second reconstructed low-pass LO_R2, reconstruct the second reconstructed high-pass HI_R2 and the second reconstructed low-pass LO_R2, and then obtain a reconstructed first low-frequency component A1`;

[0113] Perform threshold processing on the first high-frequency component D1 to obtain a reconstructed first high-frequency component D1';

[0114] The reconstructed first high-frequency component D1` is binary upsampled to obtain a first reconstructed high-pass HI_R1, and the reconstructed first low-frequency component A1` is binary upsampled to obtain a first reconstructed low-pass LO_R1. The first reconstructed high-pass HI_R1 and the first reconstructed low-pass LO_R1 are combined to obtain a denoised output signal.

[0115] Furthermore, the outlier elimination method based on wavelet transform includes:

[0116] Taking any continuous original data among the multi-source original data as the second original input signal S2, decomposing the second original input signal, and then obtaining a fourth decomposition low-pass LO_D4 and a fourth decomposition high-pass HI_D4;

[0117] Perform binary downsampling on the fourth decomposition low-pass filter LO_D4 to obtain a fourth low-frequency component A4, and perform binary downsampling on the fourth decomposition high-pass filter HI_D4 to obtain a fourth high-frequency component D4;

[0118] The fourth high-frequency component D4 is searched for the modulus maximum value, and binary downsampling is performed according to the mutation point range of the Laida criterion, that is, the data corresponding to the original data is cleared to zero;

[0119] The missing data of the second original input signal S2 is fitted to output a signal.

[0120] It should be noted that the above content can effectively eliminate the noise in the flight parameter data, identify and eliminate outliers, improve the application effect of the flight parameter data in flight training, and make the maximum interpolation error of the broken frame data not exceed 1.52% under the premise of continuous missing data, meeting the application requirements of flight training. ;

[0121] Furthermore, the method of dynamic smooth interpolation based on the rendering frame rate includes:

[0122] Dynamic smoothing methods include:

[0123] To start, initialize the rendering engine, clear the playback timer dsimtine=0 and the playback cursor Index=0;

[0124] Then determine whether the frame loop is finished;

[0125] If yes, then end the dynamic smoothing;

[0126] If not, calculate the frame cycle time deltTime and the frame playback step Δt=scalxdeltTime, then accumulate the playback time dsimtime=dsimtime+Δt, calculate the playback cursor position Index=(int)dsimt1me, and dynamically calculate the smooth difference point based on the cursor Index and the playback step Δt, and finally, update the helicopter attitude and position until the frame cycle ends.

[0127] Furthermore, the method of aligning the flight parameter time scale based on Beijing time includes:

[0128] The time scale alignment method includes:

[0129] To begin with, obtain the flight parameter time stamp in each flight parameter data;

[0130] Initialize parameters and traverse the satellite reception status bits;

[0131] Determine whether it is 0;

[0132] If yes, re-traverse the satellite receiving status bits;

[0133] If not, mark the power-on time t1 of the integrated navigation system, continue to traverse the satellite reception status bit from time t1, and then determine whether it is 1;

[0134] If not, continue to traverse the satellite receiving status bits from time t1;

[0135] If yes, mark the satellite alignment time t2 of the integrated navigation system, and calculate the difference between Beijing time and flight reference time at time t2;

[0136] After traversing all flight parameter times and calibrating them, the alignment of the flight parameter time scales can be completed.

[0137] It should be noted that by proposing a dynamic smooth interpolation method based on rendering frame rate, the interpolation points can be dynamically adjusted according to the 3D rendering frame rate to make the 3D flight playback smooth and continuous; by using the Beijing time at the satellite alignment time to correct the flight parameter time, the flight parameter time of multiple helicopters can be made continuous and unified; thus, it can solve the various problems encountered in the application of flight parameter data in flight training, such as noise and wild value interference, data interruption caused by defects, and difficulty in aligning the time scales of multiple aircraft, and effectively improve the application effect of flight parameter data in flight training;

[0138] Furthermore, the analysis results are graded and visually reviewed based on the established evaluation criteria and standard operations, and the subject evaluation and manipulation bias methods are obtained, including:

[0139] Design a criterion editor based on dynamic compilation technology, formulate the evaluation criteria for each flight subject in the criterion editor, make performance judgments based on the evaluation criteria and analysis results, and then obtain the subject evaluation for each subject;

[0140] Divide flight training into flight phases, formulate standard operations for flight actions corresponding to each flight phase, and store the standard operations in the standard operation criterion library;

[0141] Compare the standard operation with the flight operation in the visual replay to obtain the operational deviation of the flight action;

[0142] Among them, the visual review methods include:

[0143] Build 3D scenes based on Qt and OSG&OSGEarth 3D engines;

[0144] Establish a configuration interface for 3D scenes, and use the configuration interface to configure satellite images and elevation data to render the 3D scenes, thereby achieving high-precision 3D scene rendering in any scale range;

[0145] Perform technical optimization on the rendered 3D scene, including dynamic loading, level detail processing and other technical optimizations, so as to enhance the immersive feeling of flight reproduction;

[0146] Using flight parameter data, the aircraft recreates flight in a three-dimensional scene, drives instruments and cabin sounds synchronously, and observes flight trajectory, projection, and attitude from multiple perspectives and in all directions, so as to obtain a visual review of flight actions (flight parameter data). Among them, threat targets such as radar and artillery can also be set and the threat distance can be automatically calculated, thereby providing technical support for flight review and review of complex flight courses;

[0147] The flight parameter data noise elimination, outlier removal and defective data interpolation fitting methods are applied to obtain a visual review of the flight action.

[0148] It should be noted that by dividing the flight phases and evaluation standards, a theoretical foundation can be laid for flight evaluation criteria, and the flight can be reproduced by applying flight parameter data to three-dimensional scenes; the mission background can be imagined, and a method is proposed to set threat targets such as radars and anti-aircraft artillery positions on the predetermined flight route (with reserved parameter setting interface and interface for automatic simulation calculation of threat range based on terrain), and issue a warning when the helicopter enters the threat target detection and attack range, so as to assist the review crew in detecting and avoiding dangers when performing complex flight courses, and explore new ideas for flight review of complex courses.

[0149] Furthermore, the historical results of standard operations are obtained, and trend analysis of manipulation deviations is performed based on the historical results, that is, the methods of finding the problems include:

[0150] Obtain historical results of standard operations and store the historical results in a preset standard operation criterion library;

[0151] Calculate the control deviations corresponding to historical performance and current flight parameter data, and plot multiple control deviations into a trend chart in chronological order;

[0152] Find the flight parameter data with a control deviation greater than e% in the trend chart as abnormal points, and take the flight actions corresponding to the abnormal points as the problems to be found.

[0153] Furthermore, the method of obtaining improvement measures for flight actions based on the problems found, and feeding the problems and improvement measures back to the flight plan includes:

[0154] Add the flight actions corresponding to the found problems to the flight action list;

[0155] Obtain the abnormal points of the flight action corresponding to the found problem, and use the control deviation between the flight parameter data corresponding to the abnormal point and the flight parameter data of the standard operation as an improvement measure;

[0156] Feedback improvement measures to the flight plan so that the flight actions corresponding to the problem can be improved next time.

[0157] It should be noted that the above content divides the flight stages and establishes standard operations and evaluation criteria, so that each flight subject and flight actions within the flight subject can be evaluated, so as to obtain control deviations, and through trend analysis of control deviations, find out abnormal points, and provide improvement measures, which is conducive to the correction of flight actions.

[0158] In this embodiment, by applying flight parameter data noise elimination, outlier elimination and defective data interpolation fitting methods, problems such as flight playback jitter are solved, and the three-dimensional flight playback effect is effectively improved; a dynamic smooth interpolation method based on rendering frame rate and a flight parameter time-scale alignment method based on Beijing time are applied to solve the time-scale alignment problem of multi-aircraft flight parameter data, and achieve time and space unification during multi-aircraft playback; by dividing the flight phases and establishing standard operations and evaluation criteria, each flight subject and flight actions within the flight subject can be judged, so that the control deviation can be obtained, and through trend analysis of the control deviation, abnormal points can be found, and improvement measures are given to facilitate the correction of flight actions; by providing a three-dimensional scene configuration interface and configuring satellite images and elevation data, the rendering of high-precision three-dimensional scenes of arbitrary scale ranges is achieved, and the rendering effect is optimized through dynamic loading, hierarchical detail processing and other technologies, thereby enhancing the immersiveness of flight reproduction.

[0159] Example 2

[0160] See also Figure 1 and Figure 2 As shown, the flight evaluation method based on flight parameter data described in this embodiment includes the following steps:

[0161] Embodiment 2:

[0162] See also Figure 2 As shown, a flight evaluation method based on flight parameter data comprises the following steps:

[0163] Step 1: Formulate a flight plan and identify the flight subjects corresponding to the flight plan, and obtain the corresponding flight action list based on the flight subjects;

[0164] Step 2: Collect the multi-source original data corresponding to the flight action list and perform fusion processing to obtain the flight parameter data, and analyze the flight parameter data to obtain the analysis results;

[0165] Step 3: Perform grade judgment and visual review of the analysis results based on the established evaluation criteria and standard operations, and then obtain subject evaluation and manipulation deviations;

[0166] Step 4: Obtain the historical results of standard operations, and conduct trend analysis on the manipulation deviations based on the historical results to find the problems;

[0167] Step 5: Obtain improvement measures for flight actions based on the problems found, and feed the problems and improvement measures back to the flight plan.

[0168] In this embodiment, problems such as flight playback jitter are solved, and the three-dimensional flight playback effect is effectively improved; the problem of time scale alignment of multi-aircraft flight parameter data is solved, and the time and space unification of multi-aircraft playback is achieved; each flight subject and the flight actions within the flight subject can be judged, so as to obtain the control deviation, and through trend analysis of the control deviation, the abnormal points can be found, and improvement measures can be given to facilitate the correction of flight actions; by providing a three-dimensional scene configuration interface, the immersion of flight reproduction can be improved.

[0169] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0170] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0171] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0172] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A flight review system based on flight parameter data, characterized in that: include: A training task module, which is used to formulate a flight plan and determine the flight subject corresponding to the flight plan, and obtain the corresponding flight action list based on the flight subject; The data analysis module is connected to the training task module and is used to collect and fuse the multi-source original data of the flight action list corresponding to the flight action, thereby obtaining the flight parameter data, and analyzing the flight parameter data to obtain the analysis results; The review module is connected to the data analysis module and is used to judge the results and conduct a visual review of the analysis results according to the established evaluation criteria and standard operations, thereby obtaining subject evaluation and manipulation deviations; The evaluation module is connected to the review module and is used to obtain the historical results of standard operations and perform trend analysis on the manipulation deviations based on the historical results to find the problems; The review module is connected to the evaluation module, and is used to obtain improvement measures for flight actions based on the problems found, and to feed back the problems and improvement measures to the flight plan.

2. A flight commentary system based on flight parameter data according to claim 1, characterized in that: The method of formulating a flight plan, determining the flight subject corresponding to the flight plan, and obtaining the corresponding flight action list based on the flight subject includes: Develop flight plans and establish subject criteria library; The flight subjects in the flight plan are judged according to the subject judgment library, so as to obtain the flight subjects in the flight plan; Summarize the flight subjects in the flight plan and obtain a flight action list.

3. The flight commentary system based on flight parameter data according to claim 1, characterized in that: The method of collecting multi-source original data corresponding to the flight action list of the flight action and fusing them to obtain flight parameter data, and analyzing the flight parameter data to obtain the analysis results includes: Collect multi-source original data of the aircraft, establish a source database, and store the multi-source original data in the source database; The multi-source raw data are fused to obtain flight parameter data, and then the flight parameter data are dynamically smoothed and time-scale aligned, wherein the fusion process includes noise elimination and outlier removal; Establish a formula criterion library, calculate the flight parameter data based on the formula criterion library, and then obtain the analysis results; A flight day schedule database is established, and the flight plan maker manually enters the flight plan into the flight day schedule database, and stores the analysis results in the flight day schedule database.

4. A flight commentary system based on flight parameter data according to claim 3, characterized in that: The noise reduction method includes: Taking any continuous original data among the multi-source original data as the first original input signal S1, decomposing the first original input signal S1, and then obtaining a first decomposition low-pass LO_D1 and a first decomposition high-pass HI_D1; Perform binary downsampling on the first decomposition low-pass LO_D1 to obtain a first low-frequency component A1, and perform binary downsampling on the first decomposition high-pass HI_D1 to obtain a first high-frequency component D1; Decompose the first low-frequency component A1 to obtain a second decomposition low-pass component LO_D2 and a second decomposition high-pass component HI_D2; Perform binary downsampling on the second decomposition low-pass LO_D2 to obtain a second low-frequency component A2, and perform binary downsampling on the second decomposition high-pass HI_D2 to obtain a second high-frequency component D2; Decompose the second low-frequency component A2 to obtain a third decomposition low-pass filter LO_D3 and a third decomposition high-pass filter HI_D3; Perform binary downsampling on the third decomposition low-pass LO_D3 to obtain a third low-frequency component A3, and perform binary downsampling on the third decomposition high-pass HI_D3 to obtain a third high-frequency component D3; Performing threshold processing on the third low-frequency component A3 to obtain a reconstructed third low-frequency component A'3, and performing threshold processing on the third high-frequency component D3 to obtain a reconstructed third high-frequency component D'3; Perform binary upsampling on the third low-frequency component A`3 to obtain a third reconstructed low-pass LO_R3, perform binary upsampling on the third high-frequency component D`3 to obtain a third reconstructed high-pass HI_R3, reconstruct the third reconstructed low-pass LO_R3 and the third reconstructed high-pass HI_R3, and then obtain a reconstructed second low-frequency component A2`; Perform threshold processing on the second high-frequency component D2 to obtain a reconstructed second high-frequency component D2'; Perform binary upsampling on the reconstructed second high-frequency component D2` to obtain a second reconstructed high-pass HI_R2, perform binary upsampling on the reconstructed second low-frequency component A2` to obtain a second reconstructed low-pass LO_R2, reconstruct the second reconstructed high-pass HI_R2 and the second reconstructed low-pass LO_R2, and then obtain a reconstructed first low-frequency component A1`; Perform threshold processing on the first high-frequency component D1 to obtain a reconstructed first high-frequency component D1'; The reconstructed first high-frequency component D1` is binary upsampled to obtain a first reconstructed high-pass HI_R1, and the reconstructed first low-frequency component A1` is binary upsampled to obtain a first reconstructed low-pass LO_R1. The first reconstructed high-pass HI_R1 and the first reconstructed low-pass LO_R1 are combined to obtain a denoised output signal.

5. The flight commentary system based on flight parameter data according to claim 3, characterized in that: The method of removing outliers includes: Taking any continuous original data among the multi-source original data as the second original input signal S2, decomposing the second original input signal, and then obtaining a fourth decomposition low-pass LO_D4 and a fourth decomposition high-pass HI_D4; Perform binary downsampling on the fourth decomposition low-pass filter LO_D4 to obtain a fourth low-frequency component A4, and perform binary downsampling on the fourth decomposition high-pass filter HI_D4 to obtain a fourth high-frequency component D4; The fourth high-frequency component D4 is searched for the modulus maximum value, and binary downsampling is performed according to the mutation point range of the Laida criterion, that is, the data corresponding to the original data is cleared to zero; The missing data of the second original input signal S2 is fitted to output a signal.

6. The flight commentary system based on flight parameter data according to claim 3, characterized in that: The dynamic smoothing method includes: To start, initialize the rendering engine, clear the playback timer dsimtine=0, clear the playback cursor Index=0; Then determine whether the frame loop is finished; If yes, then end dynamic smoothing; If not, calculate the frame cycle time deltTime and the frame playback step Δt=scalxdeltTime, then accumulate the playback time dsimtime=dsimtime+Δt, calculate the playback cursor position Index=(int)dsimt1me, and dynamically calculate the smooth difference point based on the cursor Index and the playback step Δt, and finally update the helicopter attitude and position until the frame cycle ends; The time scale alignment method includes: To begin with, obtain the flight parameter time stamp in each flight parameter data; Initialize parameters and traverse the satellite reception status bits; Determine whether it is 0; If yes, re-traverse the satellite receiving status bits; If not, mark the power-on time t1 of the integrated navigation system, continue to traverse the satellite reception status bit from time t1, and then determine whether it is 1; If not, continue to traverse the satellite receiving status bits from time t1; If yes, mark the satellite alignment time t2 of the integrated navigation system, and calculate the difference between Beijing time and flight reference time at time t2; After traversing all flight parameter times and calibrating them, the alignment of the flight parameter time scales can be completed.

7. The flight commentary system based on flight parameter data according to claim 1, characterized in that: The method of judging the performance and visually reviewing the analysis results based on the established evaluation criteria and standard operations, and then obtaining the subject evaluation and manipulation deviation, includes: Design a criterion editor based on dynamic compilation technology, formulate the evaluation criteria for each flight subject in the criterion editor, make performance judgments based on the evaluation criteria and analysis results, and then obtain the subject evaluation for each subject; Divide flight training into flight phases, formulate standard operations for flight actions corresponding to each flight phase, and store the standard operations in the standard operation criterion library; Compare the standard operation with the flight operation in the visual replay to obtain the operational deviation of the flight action; Among them, the methods of visual review include: Build 3D scenes based on Qt and OSG&OSGEarth 3D engines; Establish a configuration interface for the 3D scene, and use the configuration interface to configure satellite images and elevation data to render the 3D scene; Perform technical optimization on rendered 3D scenes; The flight parameter data is used to drive the helicopter model to reproduce the flight in a three-dimensional scene and synchronize the flight parameter data to obtain a visual review. The flight parameter data includes driving instruments, cabin sounds, multi-viewing angles, all-round observation of flight trajectory, projection, and attitude.

8. The flight commentary system based on flight parameter data according to claim 1, characterized in that: Obtain the historical results of standard operations and conduct trend analysis on manipulation deviations based on the historical results to find the problems, including: Obtain historical results of standard operations and store the historical results in a preset standard operation criterion library; Calculate the control deviations corresponding to historical performance and current flight parameter data, and plot multiple control deviations into a trend chart in chronological order; Find the flight parameter data with a control deviation greater than e% in the trend chart as abnormal points, and take the flight actions corresponding to the abnormal points as the problems to be found.

9. A flight commentary system based on flight parameter data according to claim 8, characterized in that: The method of obtaining improvement measures for flight actions based on the problems found, and feeding back the problems and improvement measures to the flight plan, includes: Add the flight actions corresponding to the found problems to the flight action list; Obtain the abnormal points of the flight action corresponding to the found problem, and use the control deviation between the flight parameter data corresponding to the abnormal point and the flight parameter data of the standard operation as an improvement measure; Feedback improvement measures to the flight plan so that the flight actions corresponding to the problem can be improved next time.

10. A flight evaluation method based on flight parameter data, characterized in that: The method is implemented according to a flight review system based on flight parameter data as described in any one of claims 1-9.

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