Automatic identification and matching method and device for longitudinal short-period test data of simulator, computer equipment and medium

Abstract

The embodiment of the invention provides an automatic identification and fitting method and device for longitudinal short-period test data of a simulator, computer equipment and a medium, and the method comprises the following steps: preprocessing test data generated by an aircraft quality simulator, intercepting the test data, generating intercepted test data, and matching the test data with the intercepted test data; calculating test data generated per second through the intercepted test data; longitudinal short-period actions are recognized through steering column displacement data of the intercepted test data, longitudinal short-period action sections are obtained, and test data of each action section after action recognition are generated; and performing longitudinal short-period equivalent fitting on the test data after action recognition, generating fitting parameters of each action section, and evaluating flight quality data according to the flight state and fitting parameters of each action section of the longitudinal short-period action. According to the scheme, batch processing and flight quality evaluation of longitudinal short-period test data are realized, so that the efficiency of test data analysis is improved.

Description

Technical Field

[0001] This invention relates to the field of simulation technology, and in particular to a method, apparatus, computer equipment, and medium for the automatic identification and matching of longitudinal short-cycle test data in a simulator. Background Technology

[0002] Flight quality simulator tests utilize virtual simulation, electromechanical control, and visual / audio rendering to construct a near-realistic aircraft cockpit and flight motion environment, enabling human-environment flight quality assessment. The longitudinal short-cycle test method involves pilot pulse or double-pulse control of the control stick. In flight quality simulator tests, to verify that flight quality within the flight envelope meets predetermined requirements, a large number of state points need to be set, resulting in a massive amount of test data. Furthermore, during the manual processing of short-cycle test data, the data truncation position of longitudinal short-cycle actions significantly impacts subsequent equivalent fitting results; an undesirable truncation position can lead to substantial deviations in quality results. In summary, in simulator tests, manually processing longitudinal short-cycle flight quality test data is time-consuming, has low fitting accuracy, and is inefficient. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide an automatic identification and matching method for longitudinal short-cycle test data of a simulator, to solve the technical problems of long processing time, low matching accuracy, and low efficiency in the prior art when manually processing longitudinal short-cycle flight quality test data. The method includes: The test data generated by the aircraft quality simulator is preprocessed, the test data is truncated, and truncated test data is generated. The test data generated per second is calculated from the truncated test data. The truncated test data includes control stick displacement data, pitch rate data, overload data, angle of attack data, and flap deflection data. By using the control stick displacement data of the intercepted test data, the longitudinal short-cycle action is identified, and the longitudinal short-cycle action segment is obtained. By using the intercepted test data, the test data generated per second, and the longitudinal short-cycle action segment, the action identification test data of each action segment is generated. After motion recognition, the test data is subjected to longitudinal short-cycle equivalent fitting to generate flight quality evaluation parameters for each motion segment. Based on the flight status and flight quality evaluation parameters of each motion segment in the longitudinal short-cycle motion, the flight quality is evaluated.

[0004] This invention also provides an automatic identification and matching device for longitudinal short-cycle test data from a simulator, to solve the technical problems of long processing time, low matching accuracy, and low efficiency in the prior art for manually processing longitudinal short-cycle flight quality test data. The device includes: The preprocessing module is used to preprocess the test data generated by the aircraft quality simulator, truncate the test data, generate truncated test data, and calculate the test data generated per second using the truncated test data. The truncated test data includes control stick displacement data, pitch rate data, overload data, angle of attack data, and flap deflection data. The motion acquisition module is used to identify longitudinal short-cycle motions by using the control stick displacement data of the intercepted test data, to acquire longitudinal short-cycle motion segments, and to generate motion recognition test data for each motion segment by using the intercepted test data, the test data generated per second, and the longitudinal short-cycle motion segments. The quality assessment module is used to perform longitudinal short-cycle equivalent fitting on the test data after motion recognition, generate flight quality assessment parameters for each motion segment, and evaluate the flight quality based on the flight status and flight quality assessment parameters of each motion segment of the longitudinal short-cycle motion.

[0005] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for automatically identifying and matching longitudinal short-cycle test data of any simulator, thereby solving the technical problems of long processing time, low matching accuracy, and low efficiency of manual processing of longitudinal short-cycle flight quality test data in the prior art.

[0006] This invention also provides a computer-readable storage medium storing a computer program that executes the above-described automatic identification and matching method for longitudinal short-cycle test data of any of the simulators, in order to solve the technical problems of long processing time, low matching accuracy, and low efficiency of manual processing of longitudinal short-cycle flight quality test data in the prior art.

[0007] Compared with the prior art, the beneficial effects that at least one technical solution adopted in the embodiments of this specification can achieve include at least: The automatic identification and matching method of this invention improves the efficiency of processing and analyzing equivalent pitch short-period data. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1This is a flowchart of an automatic identification and matching method for longitudinal short-cycle test data of a simulator provided by an embodiment of the present invention; Figure 2 This is a flowchart of the preprocessing provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the processed test data provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the vertical short-cycle search and truncation logic provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the captured action provided in an embodiment of the present invention; Figure 6 This is a structural block diagram of a computer device provided in an embodiment of the present invention; Figure 7 This is a structural block diagram of an automatic identification and matching device for longitudinal short-cycle test data of a simulator provided in an embodiment of the present invention. Detailed Implementation

[0010] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0011] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] In this embodiment of the invention, an automatic identification and matching method for longitudinal short-cycle test data of a simulator is provided, such as... Figure 1 As shown, the method includes: Step S101: Preprocess the test data generated by the aircraft quality simulator, truncate the test data to generate truncated test data, and calculate the test data generated per second using the truncated test data. The truncated test data includes control stick displacement data, pitch rate data, overload data, angle of attack data, and flap deflection data. Step S102: Using the control stick displacement data of the intercepted test data, identify the longitudinal short-cycle action, obtain the longitudinal short-cycle action segment, and generate the action identification test data for each action segment using the intercepted test data, the test data generated per second, and the longitudinal short-cycle action segment; Step S103: Perform longitudinal short-cycle equivalent fitting on the test data after motion recognition to generate flight quality evaluation parameters for each motion segment, and evaluate the flight quality based on the flight status and flight quality evaluation parameters of each motion segment of the longitudinal short-cycle motion.

[0013] In specific implementation, the following steps are used to preprocess the test data generated by the aircraft quality simulator, truncate the test data to generate truncated test data, and calculate the test data generated per second using the truncated test data: The experimental data is sorted according to the time axis to generate time series data. Error judgment conditions are set if the time series data at a later time step is less than the time series data at the current time step, or if the difference between the time series data at a later time step and the time series data at the current time step is greater than a difference threshold. The time series data is traversed, and when the error judgment conditions are met, the time series data at the later time step is truncated to generate truncated experimental data. The total data volume L and the total time T of the truncated experimental data are compared and rounded to obtain the experimental data generated per second.

[0014] In specific implementation, the identification of short-cycle vertical movements is achieved through the following steps: obtaining short-cycle vertical movement segments; and generating post-identification test data for each movement segment using the extracted test data, the test data generated per second, and the short-cycle vertical movement segments. Using the control stick displacement data from the truncated test data, the control stick displacement action is determined to be either a pulse action or a double-pulse action, and the extreme position of the pulse action or the double-pulse action is determined. Using the extreme position of the pulse action or the double-pulse action, the pitch rate data from the truncated test data, and the control stick displacement data, the start point and end point of the pulse action or the double-pulse action are obtained. The truncated test data between the start point and the end point of the pulse action or the double-pulse action is used as the action recognition test data for the corresponding action segment.

[0015] In specific implementation, the following steps are used to determine whether the control stick displacement action is a pulse action or a double-pulse action based on the control stick displacement data obtained from the intercepted test data, and to determine the extreme position of the pulse action or the extreme position of the double-pulse action: The control stick displacement data is segmented, and the maximum absolute value of the control stick displacement data in each segment is taken. When the maximum value exceeds a set first threshold, the time corresponding to the maximum value is compared with the control stick displacement data before and after the maximum value in a first fixed number of seconds. If the difference is within a second threshold, the action type of the control stick displacement is determined, wherein the action type is a pulse action or a double pulse action. The control stick displacement data within a time period before and after the second fixed number of seconds at the position of the maximum value is taken as the control stick displacement data segment. In the control stick displacement data segment, if the maximum value of the control stick displacement data in the control stick displacement data segment is greater than the first threshold, and the minimum value of the control stick displacement data in the control stick displacement data segment is less than the negative first threshold, then the action type of the control stick displacement is a double pulse; otherwise, the action type of the control stick displacement is a pulse. If the action type is a double pulse, the time corresponding to the maximum value and the minimum value of the control stick displacement data are compared, and the extreme value of the smaller time is set as the first position. If the action type is a pulse, then the extreme value of the pulse action is set as the first position.

[0016] In specific implementation, the starting and ending points of the pulse action or double pulse action are obtained through the following steps: the extreme positions of the pulse action or double pulse action, the pitch rate data of the intercepted test data, and the stick displacement data. If the control stick displacement is a double-pulse action, the starting point of the double-pulse action is determined by iterating backward through the control stick displacement data and pitch rate data corresponding to the first extreme value of the double-pulse action, using a first fixed number of seconds as the unit. Then, the ending point of the double-pulse action is determined by iterating backward through the control stick displacement data and pitch rate data corresponding to the second extreme value of the double-pulse action, using a second fixed number of seconds as the unit. If the control stick displacement is a pulse action, the starting point of the pulse action is determined by iterating backward through the control stick displacement data and pitch rate data corresponding to the extreme value of the pulse action, using a first fixed number of seconds as the unit. Then, the ending point of the pulse action is determined by iterating backward through the extreme value of the pulse action, using a second fixed number of seconds as the unit.

[0017] In practice, the following steps are used to perform longitudinal short-cycle equivalent fitting on the experimental data after action recognition, generating fitting parameters for each action segment: Perform the following operations, one by one, until all action segments have been processed: The pitch rate data and control stick displacement data in the post-motion recognition test data are transformed from the time domain to the complex frequency domain to generate complex frequency domain pitch rate and complex frequency domain control stick displacement. Using the complex frequency domain pitch rate and complex frequency domain control stick displacement, a second-order equivalent fitting expression is used. The fitting parameters, including the short-period proportional coefficient, are obtained by fitting using the least squares method. 0:00 Time delay Damping and frequency ,in, For the complex frequency domain pitch rate, The complex frequency domain control stick displacement is used; the extreme values ​​of the angle of attack data and the extreme values ​​of the overload data in the test data after action recognition are found, and the extreme values ​​are subtracted from the initial values ​​to obtain the current segment angle of attack increment. and overload increment ; Delay the time Damping ,frequency and through formula The obtained control expectation parameter CAP is used as the flight quality evaluation parameter for the current action segment.

[0018] In practice, the flight quality is evaluated based on the flight status and flight quality assessment parameters of each action segment of the longitudinal short-cycle motion through the following steps: Based on the flap deflection data at the starting point of each action segment, determine the flight state of that action segment; obtain the flight quality evaluation parameters for each action segment; based on the flight state, obtain the corresponding longitudinal short-cycle frequency requirements, pitch response requirements, and equivalent time delay requirements from the aircraft flight quality standards; compare the desired control parameter CAP in the flight quality evaluation parameters for each action segment with the longitudinal short-cycle frequency requirements, and adjust the damping in the fitting parameters. and frequency Compare the time delay in the fitting parameters with the pitch response requirements. The flight quality assessment results are generated by comparing the results with the equivalent time delay requirements.

[0019] In one embodiment of the present invention, the method for automatic identification and matching of simulator longitudinal short-cycle test data includes the following steps: Step 1: Data preprocessing.

[0020] 1) Import the test data (including time series data, pitch rate data, and flap deflection data), iterate through the time series data, and when the time at position t0+1 is less than the time at position t0, or the time difference is greater than the threshold A, truncate all test data from position t0+1 and retain all data after that; if the condition is still not met after iterating to the end of the data, retain all test data and proceed to the next step.

[0021] 2) Compare the total amount of data L on the extracted time series data with the total time T and round down to get the amount of data per second.

[0022] The logic diagram of the data preprocessing process is as follows: Figure 2 As shown, the processed data is as follows Figure 3 As shown.

[0023] Step 2: Find and capture short-cycle actions.

[0024] 1) Divide the rod displacement data into k segments. Starting from the first segment, iterate through all the data. Take the absolute value of the i-th segment and find the maximum value M. When M exceeds the first threshold, compare M with the data of M1 and M2 at the first fixed number of seconds before and after it (which can be set to 0.5 seconds). If the difference is within the second threshold, then determine the pulse or double pulse action. Otherwise, jump to the next segment (i+1th segment) before traversing the last segment. 2) Take the rod displacement data within a time interval of two fixed seconds (which can be set to 2 seconds) before and after position M. Find the maximum value M3 and its position T3, and the minimum value M4 and its position T4 in this data. If the absolute values ​​of M3 and M4 are both greater than the first threshold, the action can be preliminarily determined to be a double pulse; otherwise, it can be preliminarily determined to be a pulse. Take the smaller value between the positions T3 and T4 of M3 and M4 and define it as P1, and the larger value as the second position P2. If it is determined to be a pulse, define the position of the peak value as the first position P1 and set P2=P1. 3) Select the stick displacement data from peak P1 to P12 2 seconds before it and iterate forward. The third position P3 is the position 0.3 seconds before the first position P1. When the stick displacement from the third position P3 to the first position P1 exceeds the third threshold, move both P3 and P1 forward by one position until all data does not exceed the threshold D. If the condition is still not met when P3 moves to the position P12, jump to segment i+1 and re-identify and judge. After the above conditions are met, select the control stick and pitch rate data from 1 second before P3 to segment P3 to judge the validity of the action. If the control stick displacement does not exceed the threshold D and the change does not exceed the threshold E and the pitch rate does not exceed the threshold F during this period, then the P3 position can be determined as the starting point of the action of this segment of data. Otherwise, jump to segment i+1 and re-identify and judge.

[0025] 4) Select the stick displacement data from P2 to P22 (2 seconds later) and iterate backwards. P4 is the position 0.5 seconds after P2. If the control stick exceeds the threshold D within the data segment from P2 to P4, move both P2 and P4 one position backwards until all data does not exceed the threshold D. If the condition is still not met when P4 moves to the position of P22, jump to segment i+1 and re-identify and judge. After the above condition is met, select the data segment from P4 to P45 (5 seconds later) and iterate backwards. Use the differential slope statistics method to statistically analyze the slope of the data within the segment from P4 to the traversed position P4+j. When the mean rate MS is greater than 0.005 or the standard deviation SS is greater than 0.1, exit. Then judge whether the pitch rate within the segment from P2 to P5 (2 seconds later) is less than the threshold G. If the condition is not met, move P2 and P5 forward one position. When the above condition is met or P5 moves to the position of P4, record the time of P5 as the end point of the action in the i-th segment.

[0026] 5) After completing the traversal of all k segments, remove duplicate values ​​from the found start and end point arrays, store the processed action positions, and complete the search and extraction of short-cycle actions.

[0027] The logic diagram for the search and extraction process is as follows: Figure 4 As shown, the action after truncating is as follows Figure 5 As shown.

[0028] Step 3: Longitudinal short-period equivalent fitting.

[0029] After finding all start and end times of the actions in the experimental data, equivalent fitting was performed for each action segment. The second-order equivalent fitting expression for the longitudinal short-cycle is as follows: , These are the expressions for pitch rate and rod displacement in the complex frequency domain, respectively. , , , , The fitting parameters represent the short-cycle proportional coefficient, zero point, time delay, damping, and frequency, respectively.

[0030]

[0031] In the time domain, the transfer function is constructed and fitted because... , , Since there are constraints (greater than 0), a nonlinear optimization method combined with the Sequential Quadratic Programming (SQP) algorithm is selected for parameter optimization iteration. The rod displacement is used as input to obtain the time domain response of the second-order equivalent fitting expression after each round of optimization. The output response is then optimized with the pitch rate data obtained from the experiment using the least squares method to finally obtain the fitting parameters for each action segment.

[0032] Step 4: Flight quality evaluation of test data.

[0033] Based on the flight status and matching parameters of the action segment, and in accordance with the flight stages and quality levels in GJB2874, the requirements for longitudinal short-cycle frequency, pitch response, and equivalent time delay are determined.

[0034] In this embodiment, a computer device is provided, such as... Figure 6 As shown, it includes a memory 601, a processor 602, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the above-mentioned method for automatically identifying and matching longitudinal short-cycle test data of any simulator.

[0035] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0036] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that performs the automatic identification and matching method for any of the above-described simulator longitudinal short-cycle test data.

[0037] Specifically, computer-readable storage media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media do not include transient media, such as modulated data signals and carrier waves.

[0038] Based on the same inventive concept, this invention also provides an automatic identification and matching device for simulator longitudinal short-cycle test data, as described in the following embodiments. Since the principle of the automatic identification and matching device for simulator longitudinal short-cycle test data is similar to that of the automatic identification and matching method for simulator longitudinal short-cycle test data, the implementation of the automatic identification and matching device for simulator longitudinal short-cycle test data can refer to the implementation of the automatic identification and matching method for simulator longitudinal short-cycle test data; repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0039] Figure 7 This is a structural block diagram of an automatic identification and matching device for longitudinal short-cycle test data of a simulator according to an embodiment of the present invention, such as... Figure 7 As shown, it includes: a preprocessing module 701, an action acquisition module 702, and the following description of the structure.

[0040] The preprocessing module 701 is used to preprocess the test data generated by the aircraft quality simulator, truncate the test data, generate truncated test data, and calculate the test data generated per second through the truncated test data. The truncated test data includes control stick displacement data, pitch rate data, overload data, angle of attack data, and flap deflection data. The motion acquisition module 702 is used to identify longitudinal short-cycle motions through the control stick displacement data of the intercepted test data, acquire longitudinal short-cycle motion segments, and generate motion recognition test data for each motion segment through the intercepted test data, the test data generated per second, and the longitudinal short-cycle motion segments. The quality assessment module 703 is used to perform longitudinal short-cycle equivalent fitting on the test data after motion recognition, generate flight quality assessment parameters for each motion segment, and assess flight quality based on the flight status and flight quality assessment parameters of each motion segment of the longitudinal short-cycle motion.

[0041] In one embodiment, the preprocessing module includes: A time series data generation unit is used to sort the experimental data according to the time axis order to generate time series data; The determination judgment condition unit is used to determine the error judgment condition as the time series data at the next time moment being less than the time series data at the current time moment, or the difference between the time series data at the next time moment and the time series data at the current time moment being greater than the difference threshold. A data extraction unit is used to traverse the time series data, and when the error judgment condition is met, to extract the time series data at the next time moment and generate the extracted test data. The data per second calculation unit is used to round down the total data volume L and the total time T on the intercepted test data to obtain the test data generated per second.

[0042] In one embodiment, the action acquisition module includes: A peak value determination unit is used to determine whether the action of the control stick displacement is a pulse action or a double pulse action based on the control stick displacement data of the intercepted test data, and to determine the extreme position of the pulse action or the extreme position of the double pulse action. The start and end units are determined to obtain the start and end points of the pulse action or the double pulse action by using the extreme positions of the pulse action or the double pulse action, the pitch rate data of the intercepted test data, and the control stick displacement data. The data acquisition unit is used to take the intercepted test data between the start point and the end point of the pulse action or the double pulse action as the action recognition test data of the corresponding action segment.

[0043] In one embodiment, the peak determination unit is further configured to segment the control stick displacement data, take the maximum absolute value of the control stick displacement data in each segment, and when the maximum value exceeds a set first threshold, compare the time corresponding to the maximum value with the control stick displacement data before and after the maximum value for a first fixed number of seconds. If the difference is within a second threshold, determine the action type of the control stick displacement, wherein the action type is a pulse action or a double pulse action. Take the control stick displacement data within a time period before and after the second fixed number of seconds at the position of the maximum value as the control stick displacement data segment. In the control stick displacement data segment, if the maximum value of the control stick displacement data in the control stick displacement data segment is greater than the first threshold, and the minimum value of the control stick displacement data in the control stick displacement data segment is less than the negative first threshold, then the action type of the control stick displacement is a double pulse; otherwise, the action type of the control stick displacement is a pulse. If the action type is a double pulse, compare the time corresponding to the maximum value and the minimum value of the control stick displacement data, and set the extreme value of the smaller time as the first position. If the action type is a pulse, then set the extreme value of the pulse action as the first position.

[0044] In one embodiment, the start and end unit is further configured to: if the control stick displacement action is a double-pulse action, determine the start point of the double-pulse action by iterating backward through the control stick displacement data and pitch rate data corresponding to the first extreme value of the double-pulse action in a first fixed number of seconds; and determine the end point of the double-pulse action by iterating backward through the control stick displacement data and pitch rate data corresponding to the second extreme value of the double-pulse action in a second fixed number of seconds; if the control stick displacement action is a pulse action, determine the start point of the pulse action by iterating backward through the control stick displacement data and pitch rate data corresponding to the extreme value of the pulse action in a first fixed number of seconds; and determine the end point of the pulse action by iterating backward through the control stick displacement data and pitch rate data corresponding to the extreme value of the pulse action in a second fixed number of seconds.

[0045] In one embodiment, the quality assessment module includes: A loop unit is used to perform the following operations, one by one, until all action segments have been processed: The complex frequency domain conversion unit is used to convert the pitch rate data and the stick displacement data in the test data after action recognition from the time domain to the complex frequency domain, and generate complex frequency domain pitch rate and complex frequency domain stick displacement. The fitting parameter calculation unit is used to calculate the fitting parameters using the complex frequency domain pitch rate and the complex frequency domain control stick displacement through a second-order equivalent fitting expression. The fitting parameters, including the short-period proportional coefficient, are obtained by fitting using the least squares method. 0:00 Time delay Damping and frequency ,in, For the complex frequency domain pitch rate, The displacement of the control stick in the complex frequency domain; The incremental calculation unit is used to find the extreme values ​​of the angle of attack data and the overload data in the test data after action recognition, and subtract the extreme values ​​from the initial values ​​to obtain the current segment angle of attack increment. and overload increment ; Parameter output unit, used to output the time delay Damping ,frequency and through formula The obtained control expectation parameter CAP is used as the flight quality evaluation parameter for the current action segment.

[0046] In one embodiment, the quality assessment module further includes: The flight state of the action segment is determined based on the flap deflection angle data at the starting point of each action segment; Obtain flight quality evaluation parameters for each action segment; Based on the flight status, the corresponding longitudinal short-cycle frequency requirements, pitch response requirements, and equivalent time delay requirements are obtained from the aircraft flight quality standards. The desired control parameter CAP in the flight quality evaluation parameters for each maneuver segment is compared with the longitudinal short-cycle frequency requirement, and the damping in the fitting parameters is compared. and frequency Compare the time delay in the fitting parameters with the pitch response requirements. The flight quality assessment results are generated by comparing the results with the equivalent time delay requirements.

[0047] The embodiments of this invention achieve the following technical effects: The identification and fitting method proposed in these embodiments aims to effectively solve the significant problems of long processing time and low fitting accuracy when manually processing longitudinal short-period test data during simulator testing. This method can significantly improve the efficiency of processing and analyzing equivalent pitch short-period data in simulator testing. Through a reasonable longitudinal short-period data extraction logic, combined with an efficient equivalent fitting method, the entire process can be automated through programming. This includes not only batch processing and accurate extraction of longitudinal short-period data, but also equivalent fitting and comprehensive evaluation of flight quality. With this method, the analysis time of test data is greatly shortened, and the accuracy of flight quality assessment is significantly improved, thus providing a strong guarantee for the overall efficiency and accuracy of simulator testing.

[0048] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automatic identification and matching of longitudinal short-cycle test data from a simulator, characterized in that, include: The test data generated by the aircraft quality simulator is preprocessed, the test data is truncated, and truncated test data is generated. The test data generated per second is calculated from the truncated test data. The truncated test data includes control stick displacement data, pitch rate data, overload data, angle of attack data, and flap deflection data. By using the control stick displacement data of the intercepted test data, the longitudinal short-cycle action is identified, and the longitudinal short-cycle action segment is obtained. By using the intercepted test data, the test data generated per second, and the longitudinal short-cycle action segment, the action identification test data of each action segment is generated. After motion recognition, the test data is subjected to longitudinal short-cycle equivalent fitting to generate flight quality evaluation parameters for each motion segment. Based on the flight status and flight quality evaluation parameters of each motion segment in the longitudinal short-cycle motion, the flight quality is evaluated.

2. The automatic identification and matching method for longitudinal short-cycle test data of the simulator as described in claim 1, characterized in that, The test data generated by the aircraft quality simulator is preprocessed, the test data is truncated to generate truncated test data, and the test data generated per second is calculated from the truncated test data, including: The experimental data are sorted according to the time axis to generate time series data; The error judgment condition is that the time series data at the next time step is less than the time series data at the current time step, or the difference between the time series data at the next time step and the time series data at the current time step is greater than the difference threshold. Traverse the time series data, and when the error judgment condition is met, extract the time series data at the next time moment to generate the extracted test data; The total data volume L and the total time T of the extracted test data are compared and rounded to obtain the test data generated per second.

3. The automatic identification and matching method for longitudinal short-cycle test data of the simulator as described in claim 1, characterized in that, Longitudinal short-cycle movements are identified to obtain longitudinal short-cycle movement segments. Based on the truncated test data, the test data generated per second, and the longitudinal short-cycle movement segments, post-action identification test data for each movement segment is generated, including: Based on the control stick displacement data of the intercepted test data, determine whether the control stick displacement action is a pulse action or a double pulse action, and determine the extreme position of the pulse action or the extreme position of the double pulse action. The starting and ending points of the pulse action or double pulse action are obtained by using the extreme positions of the pulse action or double pulse action, the pitch rate data of the intercepted test data, and the stick displacement data. The intercepted test data between the start point and the end point of the pulse action or double pulse action is used as the action recognition test data of the corresponding action segment.

4. The automatic identification and matching method for longitudinal short-cycle test data of simulators as described in claim 3, characterized in that, Based on the control stick displacement data obtained from the intercepted test data, it is determined whether the control stick displacement action is a pulse action or a double-pulse action, and the extreme position of the pulse action or the extreme position of the double-pulse action is determined, including: The control stick displacement data is segmented, and the maximum absolute value of the control stick displacement data in each segment is taken. When the maximum value exceeds a set first threshold, the time corresponding to the maximum value is compared with the control stick displacement data before and after the maximum value by a first fixed number of seconds. If the difference is within a second threshold, the action type of the control stick displacement is determined, wherein the action type is a pulse action or a double pulse action. Take the control stick displacement data within the time period before and after the second fixed number of seconds at the position of the maximum value as the stick displacement data segment; In the stick displacement data segment, if the maximum value of the stick displacement data in the stick displacement data segment is greater than the first threshold, and the minimum value of the stick displacement data in the stick displacement data segment is less than the negative first threshold, then the action type of the stick displacement is a double pulse; otherwise, the action type of the stick displacement is a pulse. If the action type is a double pulse, compare the time corresponding to the maximum value and the minimum value of the control stick displacement data, and set the extreme value of the shorter time as the first position; if the action type is a pulse, then set the extreme value of the pulse action as the first position.

5. The automatic identification and matching method for longitudinal short-cycle test data of simulators as described in claim 3, characterized in that, The starting and ending points of the pulse or double-pulse action are obtained by using the extreme positions of the pulse action or double-pulse action, the pitch rate data of the intercepted test data, and the stick displacement data, including: If the control stick displacement action is a double pulse action, the control stick displacement data and pitch rate data corresponding to the first extreme value of the double pulse action are traversed forward from the first fixed number of seconds before the first extreme value of the double pulse action to the first extreme value of the double pulse action, in units of the first fixed number of seconds, to determine the starting point of the double pulse action; Using a second fixed number of seconds as a unit, traverse backwards from the second extreme value of the double pulse action to the second fixed number of seconds after the position of the second extreme value of the double pulse action, and determine the end point of the double pulse action; If the control stick displacement action is a pulse action, the control stick displacement data and pitch rate data corresponding to the extreme value of the pulse action are traversed forward from the first fixed number of seconds before the extreme value of the pulse action to the extreme value of the pulse action in units of the first fixed number of seconds, to determine the starting point of the pulse action. Using a second fixed number of seconds as the unit, the control stick displacement data and pitch rate data corresponding to the second fixed number of seconds after the extreme value of the pulse action are traversed backward to determine the end point of the pulse action.

6. The automatic identification and matching method for simulator longitudinal short-cycle test data as described in any one of claims 1 to 5, characterized in that, Longitudinal short-cycle equivalent fitting is performed on the experimental data after action recognition to generate fitting parameters for each action segment, including: Perform the following operations, one by one, until all action segments have been processed: After motion recognition, the pitch rate data and the stick displacement data in the test data are converted from the time domain to the complex frequency domain to generate complex frequency domain pitch rate and complex frequency domain stick displacement. Using the complex frequency domain pitch rate and the complex frequency domain stick displacement, a second-order equivalent fitting expression is obtained. The fitting parameters, including the short-period proportional coefficient, are obtained by fitting using the least squares method. 0:00 Time delay Damping and frequency ,in, For the complex frequency domain pitch rate, The displacement of the control stick in the complex frequency domain; Find the extreme values ​​of the angle of attack data and the extreme values ​​of the overload data in the test data after motion recognition, and subtract the extreme values ​​from the initial values ​​to obtain the angle of attack increment for the current segment. and overload increment ; The time delay Damping ,frequency and through formula The obtained control expectation parameter CAP is used as the flight quality evaluation parameter for the current action segment.

7. The automatic identification and matching method for longitudinal short-cycle test data of simulators as described in any one of claims 1 to 5, characterized in that, Flight quality is assessed based on the flight status and flight quality evaluation parameters of each segment of the longitudinal short-cycle maneuver, including: The flight state of the action segment is determined based on the flap deflection angle data at the starting point of each action segment; Obtain flight quality evaluation parameters for each action segment; Based on the flight status, the corresponding longitudinal short-cycle frequency requirements, pitch response requirements, and equivalent time delay requirements are obtained from the aircraft flight quality standards. The desired control parameter CAP in the flight quality evaluation parameters for each maneuver segment is compared with the longitudinal short-cycle frequency requirement, and the damping in the fitting parameters is compared. and frequency Compare the time delay in the fitting parameters with the pitch response requirements. The flight quality assessment results are generated by comparing the results with the equivalent time delay requirements.

8. An automatic identification and matching device for longitudinal short-cycle test data of a simulator, characterized in that, include: The preprocessing module is used to preprocess the test data generated by the aircraft quality simulator, truncate the test data, generate truncated test data, and calculate the test data generated per second using the truncated test data. The truncated test data includes control stick displacement data, pitch rate data, angle of attack data, overload data, and flap deflection data. The motion acquisition module is used to identify longitudinal short-cycle motions by using the control stick displacement data of the intercepted test data, to acquire longitudinal short-cycle motion segments, and to generate motion recognition test data for each motion segment by using the intercepted test data, the test data generated per second, and the longitudinal short-cycle motion segments. The quality assessment module is used to perform longitudinal short-cycle equivalent fitting on the test data after motion recognition, generate flight quality assessment parameters for each motion segment, and evaluate the flight quality based on the flight status and flight quality assessment parameters of each motion segment of the longitudinal short-cycle motion.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the automatic identification and matching method for longitudinal short-cycle test data of the simulator as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs an automatic identification and matching method for simulator longitudinal short-cycle test data according to any one of claims 1 to 7.

Images