CN-122020849-A - Multi-rotor unmanned aerial vehicle flight risk prediction method based on digital simulation

CN122020849ACN 122020849 ACN122020849 ACN 122020849ACN-122020849-A

Abstract

The invention relates to the technical field of aerospace flight simulation, in particular to a multi-rotor unmanned aerial vehicle flight risk prediction method based on digital simulation, which comprises the steps of S1 to S6, wherein simulation data are collected in real time through shared memory mapping, a hierarchical criterion standard library is constructed after Kalman filtering pretreatment, monte Carlo residual analysis is introduced to carry out probability calibration on a critical value, systematic design defects are identified through triggering frequency, a risk assessment result is adjusted according to angle parameters, and the adjustment process of the risk assessment result is corrected according to a roll angle, so that false exceeding error judgment of a high maneuver region is eliminated, the assessment result is ensured to truly reflect design performance instead of numerical calculation false, the self-adaptive improvement of assessment accuracy in a full maneuver envelope is realized, and the threshold shrinkage amplitude is accurately matched with an attitude angle amplitude.

Inventors

WANG NING
LIU HONGTAO
GUO GUANGLI
ZHANG TIANHE

Assignees

华创众享(北京)信息科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260126

Claims (10)

1. The method for predicting the flight risk of the multi-rotor unmanned aerial vehicle based on the digital simulation is characterized by comprising the following steps of: Step S1, acquiring simulation operation data; step S2, preprocessing the simulation operation data to obtain the processed simulation operation data; step S3, constructing a criterion standard library, and performing risk assessment on the processed simulation operation data according to the criterion standard library to obtain a risk assessment result; S4, performing Monte Carlo residual analysis on flight parameters in the processed simulation operation data, calibrating a risk assessment result according to the analysis result, acquiring Monte Carlo residual analysis times, and optimizing a calibration process of the risk assessment result according to the Monte Carlo residual analysis times; step S5, adjusting the risk assessment result according to the angle parameters in the processed simulation operation data so as to tighten the position deviation threshold value in the criterion standard library, and correcting the adjustment process of the risk assessment result according to the roll angle in the angle parameters; and S6, pushing the risk assessment result to a multi-rotor unmanned aerial vehicle flight risk prediction terminal.
2. The method for predicting the flight risk of the multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S1 obtains simulation operation data, and specifically comprises: and acquiring operation data from the simulation engine in real time through a shared memory mapping technology, wherein the shared memory adopts a ring buffer structure, the capacity is 10 seconds of data volume, and the acquisition frequency is 100Hz.
3. The method for predicting the flight risk of the multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S2 is to preprocess the simulation operation data to obtain the processed simulation operation data, and specifically comprises: Denoising the simulation operation data to obtain denoised operation data, wherein the denoising process comprises the steps of denoising the simulation operation data by adopting a moving average method in a sliding window, wherein the window width is set to be 100ms; And carrying out fusion processing on the denoised operation data by using a Kalman filtering method to obtain the processed simulation operation data, wherein the Kalman filtering method adopts an extended Kalman filtering architecture.
4. The method for predicting the flight risk of the multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S3 of constructing the criterion standard library specifically comprises: step S301, setting an evaluation threshold data set; Step S302, respectively constructing a key flight parameter safety limit value evaluation table, a flight control and navigation positioning precision evaluation table, a limit wind-resistant safety margin evaluation table and a wind-resistant operation maintenance precision evaluation table according to various thresholds in an evaluation threshold data set; And step S303, outputting a key flight parameter safety limit value evaluation table, a flight control and navigation positioning accuracy evaluation table, a limit wind resistance safety margin evaluation table and a wind resistance operation maintenance accuracy evaluation table as a criterion standard library.
5. The method for predicting the flight risk of the multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S3 performs risk assessment on the processed simulation operation data according to a criterion standard library to obtain a risk assessment result, and specifically comprises the following steps: And comparing the flight parameters in the processed simulation operation data with an evaluation threshold data set in a criterion standard library to obtain a risk evaluation result, wherein the flight parameters comprise a maximum horizontal offset, a maximum vertical offset, a maximum horizontal standard deviation, a maximum vertical standard deviation, a maximum roll angle difference, a maximum pitch angle difference and a maximum deflection angle difference.
6. The method for predicting the flight risk of the multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S4 performs monte carlo residual analysis on the flight parameters in the processed simulation operation data, and calibrates the risk assessment result according to the analysis result, and specifically comprises: Adding uncertainty to each evaluation threshold in a criterion standard library to obtain an uncertainty evaluation value A, setting A=B+C, wherein B is the evaluation threshold, C is the uncertainty, triggering Monte Carlo residual analysis when any parameter of a flight parameter exceeds the evaluation threshold and is smaller than the uncertainty evaluation value, collecting the super-threshold probability Pc of 1000 samples of the parameter falling into an uncertainty evaluation interval, wherein the uncertainty evaluation interval is an interval formed by the evaluation threshold and the uncertainty evaluation value, comparing the super-threshold probability Pc with a preset super-threshold probability P0, judging the super-threshold condition according to a comparison result, and calibrating a risk evaluation result according to the judgment result, wherein the method comprises the following steps: when Pc is less than or equal to P0, judging that the condition of the threshold is not exceeded, and calibrating the risk assessment result, wherein the calibration comprises the steps of modifying the risk assessment result into qualified one, adding an uncertain mark, wherein the uncertain mark is that the edge passes, and suggesting to reevaluate; when Pc > P0, the condition of exceeding the threshold is judged to be out of standard, and the risk assessment result is not calibrated.
7. The method for predicting the risk of flying a multi-rotor unmanned aerial vehicle based on digital simulation according to claim 6, wherein the step S4 obtains the number of analysis of the monte carlo residual errors, and optimizes the calibration process of the risk assessment result according to the number of analysis of the monte carlo residual errors, and specifically comprises: Comparing the Monte Carlo residual analysis times A with the preset Monte Carlo residual analysis times A0, judging the triggering condition of Monte Carlo residual analysis according to the comparison result, and optimizing the calibration process of the risk assessment result according to the judgment result, wherein: when A is less than or equal to A0, determining that the Monte Carlo residual analysis triggering condition is low frequency, and not optimizing the calibration process of the risk assessment result; when A > A0, judging that the Monte Carlo residual analysis triggering condition is high frequency, and optimizing the calibration process of the risk assessment result, wherein the optimization comprises the steps of adding optimization labels taking the multi-rotor unmanned aerial vehicle design allowance as the content and suggesting the optimization control law on the basis of calibrating the risk assessment result.
8. The method for predicting the risk of flying a multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S5 adjusts the risk assessment result according to the angle parameter in the processed simulation operation data, so as to tighten the position offset threshold in the criterion standard library, and specifically comprises: When the absolute value of a roll angle hg in an angle parameter is |hg| >60 degrees and the absolute value of a pitch angle fg in the angle parameter is |fg| >60 degrees, adjusting a risk assessment result, tightening a position deviation threshold value in a criterion standard library from 1.5 meters to 1.45 meters to obtain a tightening position deviation threshold value, comparing the position deviation value in a flight parameter with the tightening position deviation threshold value again to obtain an adjusted risk assessment result, and replacing the content of the risk assessment result with the content of the adjusted risk assessment result, wherein the content of the adjusted risk assessment result comprises an adjustment mark, and the adjustment mark comprises high maneuvering region numerical drift compensation and an effective threshold value of 1.45 meters; Wherein |hg| >60 ° and |fg| >60 ° represent that the multi-rotor unmanned aerial vehicle is performing high maneuver, where the kalman filtering method may produce errors.
9. The method for predicting the risk of flying a multi-rotor unmanned aerial vehicle based on digital simulation according to claim 8, wherein the step S5 of calibrating the adjustment process of the risk assessment result according to the roll angle in the angle parameter specifically comprises: When the angle of the tightening position deviation threshold value is less than or equal to 70 DEG and less than or equal to |hg| <80 DEG, the adjustment process of the risk assessment result is checked, and the tightening position deviation threshold value is modified to be 1.38 meters; when the absolute value of hg is more than or equal to 80 degrees, the adjustment process of the risk assessment result is checked to modify the tightening position deviation threshold value to be 1.275 meters; Wherein, 70 DEG is less than or equal to |hg| <80 DEG represents moderate high maneuver of the multi-rotor unmanned aerial vehicle in high maneuver operation, and |hg|is more than or equal to 80 DEG represents heavy high maneuver of the multi-rotor unmanned aerial vehicle in high maneuver operation.
10. The method for predicting the risk of flying a multi-rotor unmanned aerial vehicle based on digital simulation according to claim 1, wherein the step S6 pushes the risk assessment result to a multi-rotor unmanned aerial vehicle risk prediction terminal, and specifically comprises: Generating an evaluation card and an evaluation curve according to the risk evaluation result, and pushing the evaluation card and the evaluation curve to a multi-rotor unmanned aerial vehicle flight risk prediction terminal through wireless signals; the evaluation card comprises a specification of an out-of-standard item in a risk evaluation result, a risk entropy value, monte Carlo triggering times, an adjustment mark and a tightening position deviation threshold; the evaluation curve includes an evaluation threshold curve and a flight parameter curve.

Description

Multi-rotor unmanned aerial vehicle flight risk prediction method based on digital simulation Technical Field The invention relates to the technical field of aerospace flight simulation, in particular to a multi-rotor unmanned aerial vehicle flight risk prediction method based on digital simulation. Background With low-altitude economic development, multi-rotor unmanned aerial vehicle application is widened, and flight safety simulation evaluation is a key for guaranteeing reliable operation of the multi-rotor unmanned aerial vehicle. The multi-rotor unmanned aerial vehicle flight safety assessment relies on simulation testing and post-hoc manual analysis. In the prior art, a fixed threshold static criterion is adopted, so that the method cannot adapt to numerical drift caused by the accumulation of Kalman filtering linearization errors under a high maneuvering working condition, and the false exceeding error judgment rate is up to more than 12%. The Chinese patent with publication number CN114595519A discloses a flight simulation system, a method, equipment and a medium, in particular relates to the technical field of aerospace flight simulation, the method determines the elastic deformation of a landing gear by acquiring the state parameters of the aircraft, determines the stress state information of the aircraft based on the state parameters of the aircraft, and transmits the stress state information to an aircraft simulation system to realize the ground simulation of the flight simulation of the aircraft, so that the stress state information of the aircraft can be determined by acquiring the parameters of the aircraft and adjusting a small number of parameters (the characteristic parameters and the friction characteristic parameters of the landing gear), the ground simulation of the flight simulation is realized, the method can be suitable for various aircraft adopting the landing gear system by adjusting the parameters, has strong universality, shortens the simulation cost, shortens the research and development period, and is favorable for the research and development of the aircrafts such as unmanned aerial vehicles. However, the scheme still has the problem that the scheme cannot adapt to numerical calculation errors under the high maneuvering condition, so that the false out-of-standard erroneous judgment rate of the pure digital simulation evaluation result is high. Disclosure of Invention Therefore, the invention provides a multi-rotor unmanned aerial vehicle flight risk prediction method based on digital simulation, which is used for solving the problem that the false out-of-standard error judgment rate of a pure digital simulation evaluation result is high because the numerical calculation error under a high maneuvering working condition cannot be adapted in the prior art. In order to achieve the above purpose, the present invention provides a method for predicting the flight risk of a multi-rotor unmanned aerial vehicle based on digital simulation, comprising: Step S1, acquiring simulation operation data; step S2, preprocessing the simulation operation data to obtain the processed simulation operation data; step S3, constructing a criterion standard library, and performing risk assessment on the processed simulation operation data according to the criterion standard library to obtain a risk assessment result; S4, performing Monte Carlo residual analysis on flight parameters in the processed simulation operation data, calibrating a risk assessment result according to the analysis result, acquiring Monte Carlo residual analysis times, and optimizing a calibration process of the risk assessment result according to the Monte Carlo residual analysis times; step S5, adjusting the risk assessment result according to the angle parameters in the processed simulation operation data so as to tighten the position deviation threshold value in the criterion standard library, and correcting the adjustment process of the risk assessment result according to the roll angle in the angle parameters; and S6, pushing the risk assessment result to a multi-rotor unmanned aerial vehicle flight risk prediction terminal. Further, the step S1 of acquiring simulation operation data specifically includes: and acquiring operation data from the simulation engine in real time through a shared memory mapping technology, wherein the shared memory adopts a ring buffer structure, the capacity is 10 seconds of data volume, and the acquisition frequency is 100Hz. Further, the step S2 of preprocessing the simulation operation data to obtain the processed simulation operation data specifically includes: Denoising the simulation operation data to obtain denoised operation data, wherein the denoising process comprises the steps of denoising the simulation operation data by adopting a moving average method in a sliding window, wherein the window width is set to be 100ms; And carrying out fusion processing on the denoised operation data b