Aerial vehicle identification method and system based on VR

Abstract

The present invention relates to the field of virtual reality model technology, and specifically to an interactive method and system for aerial aircraft identification training based on VR, the method comprising obtaining first data; the first data comprising a pre-set simulation time period and a data sampling time interval; reading a simulation result of an aerial aircraft VR model obtained by pre-simulation; extracting data from the simulation result of the aerial aircraft VR model within the simulation time period according to the data sampling time interval to obtain a data sequence of the remaining fuel volume of the aircraft, a data sequence of the aircraft speed, and a data sequence of the length of the aircraft trajectory; using a flight data comparison algorithm to compare and calculate the data sequence of the remaining fuel volume of the aircraft, the data sequence of the aircraft speed, and the data sequence of the length of the aircraft trajectory, to obtain a comparison mark and to identify the correctness of the aerial aircraft VR model. The present invention analyzes and calculates the simulation result data of the aerial aircraft VR model, thereby identifying whether the simulation result is correct.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual reality models, and in particular to a method and system for identifying aerial vehicles based on VR. Background Art

[0002] VR models of aerial vehicles have been widely used in flight training. In the prior art, most of the research on VR models of aerial vehicles focuses on the degree of visual simulation of the model, while there is a lack of research on the simulation accuracy of the aerodynamics of the model. As a result, most VR models of aerial vehicles can only meet the visual experience, lack high-precision simulation of the aerodynamics of the real environment, and it is difficult to meet the needs of flight training. Therefore, it is urgent to identify whether the calculation results of the aircraft dynamics simulation are correct, and use accurate simulation results for flight training.

[0003] The existing technology mainly obtains the aircraft power model and uses the dynamic calculation method to identify whether the simulation calculation results of the aircraft speed data, aircraft trajectory data and aircraft fuel consumption data are correct. However, the existing technology has the following problems: First, the dynamic calculation needs to rely on a large amount of basic data, including aircraft engine model data, aerodynamic data, etc., but the VR model of the aircraft in the air is often unable to provide these data to the user, so it is impossible to identify whether the aircraft dynamics simulation calculation results are correct through dynamic calculation. Second, the dynamic calculation is often very complex and it is difficult to meet the needs of efficient and rapid identification.

[0004] In recent years, with the continuous development of big data technology, model identification through data mining has become a feasible technical means. Therefore, in order to overcome the drawbacks of existing technical solutions, it is necessary to treat the aerial aircraft VR model as a "black box model", starting from the output data of the aerial aircraft VR model simulation results, and use big data algorithms to identify the model to determine whether the model is correct. Summary of the invention

[0005] (1) Technical issues to be resolved

[0006] The purpose of the present invention is to provide a method and system for identifying an aerial vehicle based on VR, so as to identify whether the simulation result of the VR model of the aerial vehicle is correct.

[0007] (2) Technical solution

[0008] To achieve the above object, the present invention provides a method for identifying aerial vehicles based on VR, the method comprising the following steps:

[0009] S1, obtaining first data; the first data includes a preset simulation time period and a data sampling time interval.

[0010] S2, reading the simulation results of the VR model of the aerial aircraft obtained by pre-simulation; extracting data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval, and obtaining the data sequence of the remaining fuel volume of the aircraft, the data sequence of the aircraft speed, and the data sequence of the aircraft trajectory length.

[0011] S3, using a flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence to obtain a comparison mark.

[0012] S4, identifying the correctness of the VR model of the aerial vehicle according to the comparison mark, and outputting the identification result to the training personnel.

[0013] Furthermore, the method of extracting data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval to obtain the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence includes:

[0014] The change curve of aircraft tank weight over time, the change curve of aircraft speed over time, and the change curve of aircraft flight track length over time are read from the simulation results of the aircraft VR model in the air.

[0015] The values ​​of the curve of the change of the aircraft fuel tank weight over time are sampled according to the data sampling time interval to obtain the aircraft remaining fuel data sequence; the values ​​of the curve of the change of the aircraft speed over time are sampled according to the data sampling time interval to obtain the aircraft speed data sequence; the values ​​of the curve of the change of the aircraft flight trajectory length over time are sampled according to the data sampling time interval to obtain the aircraft trajectory length data sequence; the lengths of the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence are equal, which is recorded as the first data length .

[0016] Furthermore, the method of using the flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence to obtain the comparison mark includes:

[0017] The aircraft fuel quantity change data sequence is calculated based on the aircraft remaining fuel quantity data sequence; the calculation formula of the aircraft fuel quantity change data sequence is:

[0018] ;

[0019] in, Indicates the data number, which is a value from 1 to integer variable of ; The number of aircraft fuel quantity change data series Data, in kilograms; The number of remaining fuel in the aircraft data sequence individual data; The number of remaining fuel in the aircraft data sequence data.

[0020] The aircraft trajectory length change data sequence is calculated based on the aircraft trajectory length data sequence; the calculation formula of the aircraft trajectory length change data sequence is:

[0021] ;

[0022] in, is the first Data, in meters; is the first individual data; is the first data.

[0023] The aircraft speed change data sequence is calculated based on the aircraft speed data sequence; the calculation formula of the aircraft speed change data sequence is:

[0024] ;

[0025] in, is the first Data, in meters per second; is the first individual data; is the first data.

[0026] The aircraft average speed data sequence is calculated based on the aircraft speed data sequence; the calculation formula of the aircraft average speed data sequence is:

[0027] ;

[0028] in, is the number in the aircraft average speed data series The data is in meters per second.

[0029] The pre-collected aircraft output power samples and aircraft fuel quantity change samples are read; based on the aircraft output power samples and aircraft fuel quantity change samples, a nonlinear fitting algorithm is used to fit the functional relationship between the aircraft output power and the aircraft fuel quantity change, which is recorded as the first function.

[0030] The fuel quantity and speed cross sequence is calculated based on the aircraft fuel quantity change data sequence, the aircraft average speed data sequence and the first function; the calculation formula of the fuel quantity and speed cross sequence is:

[0031] ;

[0032] in, is the first in the oil speed cross sequence individual data; represents the first function.

[0033] Search for the maximum value in the oil volume and speed cross sequence, and record it as the first maximum value ; Search for the minimum value in the oil volume and speed cross sequence, recorded as the first minimum value ; Search for the maximum value in the aircraft speed change data sequence, record it as the second maximum value ; Search for the minimum value in the aircraft speed change data sequence, recorded as the second minimum ; Search for the maximum value in the aircraft average speed data sequence, recorded as the third maximum value ; Search for the minimum value in the aircraft average speed data sequence, recorded as the third minimum ; Search for the maximum value in the aircraft trajectory length change data sequence, recorded as the fourth maximum value ; Search for the minimum value in the data sequence of the change in the aircraft trajectory length, recorded as the fourth minimum .

[0034] According to the first maximum value, the first minimum value, the second maximum value, the second minimum value, the third maximum value, the third minimum value, the fourth maximum value, the fourth minimum value, the aircraft trajectory length change data sequence, the aircraft speed change data sequence, the aircraft average speed data sequence, and the fuel speed cross sequence, a normalization formula is used to calculate the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence; the normalization formula is:

[0035] ;

[0036] in, is the first sequence in the first alignment individual data; is the first sequence in the second alignment individual data; is the first individual data; is the fourth aligned sequence data.

[0037] According to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence, a comparison mark is calculated using a comparison feature algorithm. .

[0038] Furthermore, the comparison feature algorithm is used to calculate the comparison mark according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence. The methods include:

[0039] The alignment factor is calculated based on the first alignment sequence, the second alignment sequence, the third alignment sequence, and the fourth alignment sequence. ; The calculation formula of the comparison factor is:

[0040] ;

[0041] in, is the first mean, is the second mean, is the third mean, is the fourth mean, is the first factor, is the second factor.

[0042] The comparison mark is calculated according to the comparison factor; the calculation formula of the comparison mark is:

[0043] ;

[0044] in, is a preset comparison threshold.

[0045] Furthermore, the method of identifying the correctness of the VR model of the aerial vehicle according to the comparison mark and outputting the identification result to the training personnel includes:

[0046] The correctness of the VR model of the aerial vehicle is identified based on the value of the comparison mark. If the value of the comparison mark is 1, "VR model is correct" is output to the training personnel through the human-machine interface. If the value of the comparison mark is 0, "VR model is wrong" is output to the training personnel through the human-machine interface.

[0047] Based on the same inventive concept, on the other hand, the present invention also provides an aerial vehicle identification system based on VR, the system comprising:

[0048] The first data reading module is used to obtain first data; the first data includes a preset simulation time period and a data sampling time interval.

[0049] The simulation data reading module is connected to the first data reading module and is used to read the simulation results of the VR model of the aerial aircraft obtained by pre-simulation; data extraction is performed on the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval to obtain the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence.

[0050] The comparison calculation module is connected to the simulation data reading module and is used to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence using a flight data comparison algorithm to obtain a comparison mark.

[0051] The recognition module is connected to the comparison calculation module and is used to identify the correctness of the VR model of the aerial vehicle according to the comparison mark, and output the recognition result to the training personnel.

[0052] Furthermore, the simulation data reading module includes:

[0053] The curve reading module is used to read the change curve of the aircraft tank weight over time, the change curve of the aircraft speed over time, and the change curve of the aircraft flight track length over time from the simulation results of the aerial aircraft VR model.

[0054] The data sampling module is connected to the curve reading module and is used to sample the values ​​of the aircraft fuel tank weight change curve over time according to the data sampling time interval to obtain the aircraft remaining fuel data sequence; sample the values ​​of the aircraft driving speed change curve over time according to the data sampling time interval to obtain the aircraft speed data sequence; sample the values ​​of the aircraft flight trajectory length change curve over time according to the data sampling time interval to obtain the aircraft trajectory length data sequence; the lengths of the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence are equal, which is recorded as the first data length .

[0055] Furthermore, the comparison calculation module includes:

[0056] The aircraft fuel quantity change data sequence calculation module is used to calculate the aircraft fuel quantity change data sequence according to the aircraft remaining fuel quantity data sequence; the calculation formula of the aircraft fuel quantity change data sequence is:

[0057] ;

[0058] in, Indicates the data number, which is a value from 1 to integer variable of ; The number of aircraft fuel quantity change data series Data, in kilograms; The number of remaining fuel in the aircraft data sequence individual data; The number of remaining fuel in the aircraft data sequence data.

[0059] The aircraft trajectory length change data sequence calculation module is connected to the aircraft fuel quantity change data sequence calculation module and is used to calculate the aircraft trajectory length change data sequence according to the aircraft trajectory length data sequence; the calculation formula of the aircraft trajectory length change data sequence is:

[0060] ;

[0061] in, is the first Data, in meters; is the first individual data; is the first data.

[0062] The aircraft speed change data sequence calculation module is connected to the aircraft trajectory length change data sequence calculation module and is used to calculate the aircraft speed change data sequence according to the aircraft speed data sequence; the calculation formula of the aircraft speed change data sequence is:

[0063] ;

[0064] in, is the first Data, in meters per second; is the first individual data; is the first data.

[0065] The aircraft average speed data sequence calculation module is connected to the aircraft speed change data sequence calculation module and is used to calculate the aircraft average speed data sequence according to the aircraft speed data sequence; the calculation formula of the aircraft average speed data sequence is:

[0066] ;

[0067] in, is the number in the aircraft average speed data series The data is in meters per second.

[0068] The first function fitting module is connected to the aircraft average speed data sequence calculation module, and is used to read the pre-collected aircraft output power samples and aircraft fuel quantity change samples; based on the aircraft output power samples and aircraft fuel quantity change samples, a nonlinear fitting algorithm is used to fit the functional relationship between the aircraft output power and the aircraft fuel quantity change, which is recorded as the first function.

[0069] The fuel quantity and speed cross sequence calculation module is connected to the first function fitting module and is used to calculate the fuel quantity and speed cross sequence according to the aircraft fuel quantity change data sequence, the aircraft average speed data sequence and the first function; the calculation formula of the fuel quantity and speed cross sequence is:

[0070] ;

[0071] in, is the first in the oil speed cross sequence individual data; represents the first function.

[0072] The extreme value calculation module is connected to the oil quantity and speed cross sequence calculation module to search for the maximum value in the oil quantity and speed cross sequence, which is recorded as the first maximum value. ; Search for the minimum value in the oil volume and speed cross sequence, recorded as the first minimum value ; Search for the maximum value in the aircraft speed change data sequence, record it as the second maximum value ; Search for the minimum value in the aircraft speed change data sequence, recorded as the second minimum ; Search for the maximum value in the aircraft average speed data sequence, recorded as the third maximum value ; Search for the minimum value in the aircraft average speed data sequence, recorded as the third minimum ; Search for the maximum value in the aircraft trajectory length change data sequence, recorded as the fourth maximum value ; Search for the minimum value in the data sequence of the change in the aircraft trajectory length, recorded as the fourth minimum .

[0073] The normalization module is connected to the extreme value calculation module, and is used to calculate the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence according to the first maximum value, the first minimum value, the second maximum value, the second minimum value, the third maximum value, the third minimum value, the fourth maximum value, the fourth minimum value, the aircraft trajectory length change data sequence, the aircraft speed change data sequence, the aircraft average speed data sequence, and the fuel speed cross sequence using a normalization formula; the normalization formula is:

[0074] ;

[0075] in, is the first sequence in the first alignment individual data; is the first sequence in the second alignment individual data; is the third aligned sequence individual data; is the fourth aligned sequence data.

[0076] A comparison feature algorithm module is connected to the normalization module and is used to calculate the comparison mark according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence using the comparison feature algorithm. .

[0077] Furthermore, the comparison feature algorithm module includes:

[0078] A comparison calculation module, used to calculate the comparison factor according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence ; The calculation formula of the comparison factor is:

[0079] ;

[0080] in, is the first mean, is the second mean, is the third mean, is the fourth mean, is the first factor, is the second factor.

[0081] The comparison mark calculation module is connected to the comparison calculation module and is used to calculate the comparison mark according to the comparison factor; the calculation formula of the comparison mark is:

[0082] ;

[0083] in, is a preset comparison threshold.

[0084] Furthermore, the identification module includes:

[0085] The identification output module is used to identify the correctness of the VR model of the aerial vehicle according to the value of the comparison mark. If the value of the comparison mark is 1, "the VR model is correct" is output to the training personnel through the human-machine interface; if the value of the comparison mark is 0, "the VR model is wrong" is output to the training personnel through the human-machine interface.

[0086] (3) Beneficial effects

[0087] Compared with the prior art, the present invention has the following beneficial effects:

[0088] By using the flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, aircraft speed data sequence, and aircraft trajectory length data sequence to obtain a comparison mark, accurate recognition of the simulation results of the VR model of the aerial aircraft can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 This is a flowchart of a method for identifying an aerial vehicle based on VR according to Embodiment 1 of the present invention;

[0090] Figure 2 This is a schematic diagram of the module composition of the VR-based aerial vehicle identification system according to Example 2 of the present invention. DETAILED DESCRIPTION

[0091] 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.

[0092] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is applied to the accurate recognition of the simulation results of the VR model of an aerial vehicle.

[0093] Example 1: Figure 1 As shown, this embodiment provides a method for identifying aerial vehicles based on VR, and the method includes the following steps:

[0094] S1, obtaining first data; the first data includes a preset simulation time period and a data sampling time interval.

[0095] Exemplarily, the simulation time period is 1200 seconds and the data sampling time interval is 10 seconds.

[0096] S2, reading the simulation results of the VR model of the aerial aircraft obtained by pre-simulation; extracting data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval, and obtaining the data sequence of the remaining fuel volume of the aircraft, the data sequence of the aircraft speed, and the data sequence of the aircraft trajectory length.

[0097] Exemplarily, this embodiment is used to identify the accuracy of the simulation results of aircraft fuel consumption data, aircraft speed data, and aircraft trajectory data in the aerial aircraft VR model, so it is necessary to first read the simulation results of the aerial aircraft VR model. The simulation results are read from the output interface of the GF Data Simulator aerial aircraft VR model simulation platform. The data of the aerial aircraft VR model simulation results within 1200 seconds is extracted with a data sampling time interval of 10 seconds to obtain the aircraft remaining fuel data sequence, aircraft speed data sequence, and aircraft trajectory length data sequence.

[0098] S3, using a flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence to obtain a comparison mark.

[0099] S4, identifying the correctness of the VR model of the aerial vehicle according to the comparison mark, and outputting the identification result to the training personnel.

[0100] Furthermore, the method of extracting data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval to obtain the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence includes:

[0101] The change curve of aircraft tank weight over time, the change curve of aircraft speed over time, and the change curve of aircraft flight track length over time are read from the simulation results of the aircraft VR model in the air.

[0102] For example, a simulation time series is read from the GF Data Simulator platform, and the simulation time series has a step length of 0.1 second. The values ​​of the aircraft fuel tank weight, aircraft speed, and aircraft flight track length at the corresponding time of the simulation time series are read, and a fitting method is used to fit the change curve of the aircraft fuel tank weight over time, the change curve of the aircraft speed over time, and the change curve of the aircraft flight track length over time.

[0103] The values ​​of the curve of the change of the aircraft fuel tank weight over time are sampled according to the data sampling time interval to obtain the aircraft remaining fuel data sequence; the values ​​of the curve of the change of the aircraft speed over time are sampled according to the data sampling time interval to obtain the aircraft speed data sequence; the values ​​of the curve of the change of the aircraft flight trajectory length over time are sampled according to the data sampling time interval to obtain the aircraft trajectory length data sequence; the lengths of the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence are equal, which is recorded as the first data length .

[0104] The data sampling interval is 10 seconds, and the values ​​of the aircraft fuel tank weight change curve over time, the aircraft speed change curve over time, and the aircraft flight track length change curve over time are sampled to obtain the aircraft remaining fuel data sequence, aircraft speed data sequence, and aircraft track length data sequence. The lengths of the aircraft remaining fuel data sequence, aircraft speed data sequence, and aircraft track length data sequence are all 121, that is, .

[0105] Furthermore, the method of using the flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence to obtain the comparison mark includes:

[0106] The aircraft fuel quantity change data sequence is calculated based on the aircraft remaining fuel quantity data sequence; the calculation formula of the aircraft fuel quantity change data sequence is:

[0107] ;

[0108] in, Indicates the data number, which is a value from 1 to integer variable of ; The number of aircraft fuel quantity change data series Data, in kilograms; The number of remaining fuel in the aircraft data sequence individual data; The number of remaining fuel in the aircraft data sequence data.

[0109] The aircraft trajectory length change data sequence is calculated based on the aircraft trajectory length data sequence; the calculation formula of the aircraft trajectory length change data sequence is:

[0110] ;

[0111] in, is the first Data, in meters; is the first individual data; is the first data.

[0112] The aircraft speed change data sequence is calculated based on the aircraft speed data sequence; the calculation formula of the aircraft speed change data sequence is:

[0113] ;

[0114] in, is the first Data, in meters per second; is the first individual data; is the first data.

[0115] The aircraft average speed data sequence is calculated based on the aircraft speed data sequence; the calculation formula of the aircraft average speed data sequence is:

[0116] ;

[0117] in, is the number in the aircraft average speed data series The data is in meters per second.

[0118] For example, the calculation is to , to , to , to .

[0119] The pre-collected aircraft output power samples and aircraft fuel quantity change samples are read; based on the aircraft output power samples and aircraft fuel quantity change samples, a nonlinear fitting algorithm is used to fit the functional relationship between the aircraft output power and the aircraft fuel quantity change, which is recorded as the first function.

[0120] For example, the aircraft output power sample and the aircraft fuel quantity change sample can be obtained through experiments. The aircraft fuel quantity change sample refers to the aircraft fuel consumption sample within the data sampling time interval. The aircraft output power sample refers to the average value of the aircraft output power within the data sampling time interval.

[0121] The fuel quantity and speed cross sequence is calculated based on the aircraft fuel quantity change data sequence, the aircraft average speed data sequence and the first function; the calculation formula of the fuel quantity and speed cross sequence is:

[0122] ;

[0123] in, is the first in the oil speed cross sequence individual data; represents the first function.

[0124] For example, the calculation is to The purpose of calculating the fuel speed cross sequence is to construct a variable that should have a linear relationship with the aircraft speed change data sequence from the perspective of dynamics.

[0125] Search for the maximum value in the oil volume and speed cross sequence, and record it as the first maximum value ; Search for the minimum value in the oil volume and speed cross sequence, recorded as the first minimum value ; Search for the maximum value in the aircraft speed change data sequence, record it as the second maximum value ; Search for the minimum value in the aircraft speed change data sequence, recorded as the second minimum ; Search for the maximum value in the aircraft average speed data sequence, recorded as the third maximum value ; Search for the minimum value in the aircraft average speed data sequence, recorded as the third minimum ; Search for the maximum value in the aircraft trajectory length change data sequence, recorded as the fourth maximum value ; Search for the minimum value in the data sequence of the change in the aircraft trajectory length, recorded as the fourth minimum .

[0126] According to the first maximum value, the first minimum value, the second maximum value, the second minimum value, the third maximum value, the third minimum value, the fourth maximum value, the fourth minimum value, the aircraft trajectory length change data sequence, the aircraft speed change data sequence, the aircraft average speed data sequence, and the fuel speed cross sequence, a normalization formula is used to calculate the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence; the normalization formula is:

[0127] ;

[0128] in, is the first sequence in the first alignment individual data; is the first sequence in the second alignment individual data; is the first individual data; is the fourth aligned sequence data.

[0129] According to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence, a comparison mark is calculated using a comparison feature algorithm. .

[0130] Furthermore, the comparison feature algorithm is used to calculate the comparison mark according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence. The methods include:

[0131] The alignment factor is calculated based on the first alignment sequence, the second alignment sequence, the third alignment sequence, and the fourth alignment sequence. ; The calculation formula of the comparison factor is:

[0132] ;

[0133] in, is the first mean, is the second mean, is the third mean, is the fourth mean, is the first factor, is the second factor.

[0134] The comparison mark is calculated according to the comparison factor; the calculation formula of the comparison mark is:

[0135] ;

[0136] in, is a preset comparison threshold.

[0137] For example, the preset comparison threshold Calculated ,therefore .

[0138] Furthermore, the method of identifying the correctness of the VR model of the aerial vehicle according to the comparison mark and outputting the identification result to the training personnel includes:

[0139] The correctness of the VR model of the aerial vehicle is identified based on the value of the comparison mark. If the value of the comparison mark is 1, "VR model is correct" is output to the training personnel through the human-machine interface. If the value of the comparison mark is 0, "VR model is wrong" is output to the training personnel through the human-machine interface.

[0140] For example, since the value of the comparison flag is 1, "VR model is correct" is output to the trainer through the human-machine interface, which indicates that the simulation result of the VR model of the aerial vehicle satisfies the law of dynamics.

[0141] Embodiment 2: Based on the same inventive concept, Figure 2 As shown, this embodiment also provides an aerial vehicle identification system based on VR, the system comprising:

[0142] The first data reading module is used to obtain first data; the first data includes a preset simulation time period and a data sampling time interval.

[0143] The simulation data reading module is connected to the first data reading module and is used to read the simulation results of the VR model of the aerial aircraft obtained by pre-simulation; data extraction is performed on the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval to obtain the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence.

[0144] The comparison calculation module is connected to the simulation data reading module and is used to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence using a flight data comparison algorithm to obtain a comparison mark.

[0145] The recognition module is connected to the comparison calculation module and is used to identify the correctness of the VR model of the aerial vehicle according to the comparison mark, and output the recognition result to the training personnel.

[0146] Furthermore, the simulation data reading module includes:

[0147] The curve reading module is used to read the change curve of the aircraft tank weight over time, the change curve of the aircraft speed over time, and the change curve of the aircraft flight track length over time from the simulation results of the aerial aircraft VR model.

[0148] The data sampling module is connected to the curve reading module and is used to sample the values ​​of the aircraft fuel tank weight change curve over time according to the data sampling time interval to obtain the aircraft remaining fuel data sequence; sample the values ​​of the aircraft driving speed change curve over time according to the data sampling time interval to obtain the aircraft speed data sequence; sample the values ​​of the aircraft flight trajectory length change curve over time according to the data sampling time interval to obtain the aircraft trajectory length data sequence; the lengths of the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence are equal, which is recorded as the first data length .

[0149] Furthermore, the comparison calculation module includes:

[0150] The aircraft fuel quantity change data sequence calculation module is used to calculate the aircraft fuel quantity change data sequence according to the aircraft remaining fuel quantity data sequence; the calculation formula of the aircraft fuel quantity change data sequence is:

[0151] ;

[0152] in, Indicates the data number, which can be a value from 1 to integer variable of ; The number of aircraft fuel quantity change data series Data, in kilograms; The number of remaining fuel in the aircraft data sequence individual data; The number of remaining fuel in the aircraft data sequence data.

[0153] The aircraft trajectory length change data sequence calculation module is connected to the aircraft fuel quantity change data sequence calculation module and is used to calculate the aircraft trajectory length change data sequence according to the aircraft trajectory length data sequence; the calculation formula of the aircraft trajectory length change data sequence is:

[0154] ;

[0155] in, is the first Data, in meters; is the first individual data; is the first data.

[0156] The aircraft speed change data sequence calculation module is connected to the aircraft trajectory length change data sequence calculation module and is used to calculate the aircraft speed change data sequence according to the aircraft speed data sequence; the calculation formula of the aircraft speed change data sequence is:

[0157] ;

[0158] in, is the first Data, in meters per second; is the first individual data; is the first data.

[0159] The aircraft average speed data sequence calculation module is connected to the aircraft speed change data sequence calculation module and is used to calculate the aircraft average speed data sequence according to the aircraft speed data sequence; the calculation formula of the aircraft average speed data sequence is:

[0160] ;

[0161] in, is the number in the aircraft average speed data series The data is in meters per second.

[0162] The first function fitting module is connected to the aircraft average speed data sequence calculation module, and is used to read the pre-collected aircraft output power samples and aircraft fuel quantity change samples; based on the aircraft output power samples and aircraft fuel quantity change samples, a nonlinear fitting algorithm is used to fit the functional relationship between the aircraft output power and the aircraft fuel quantity change, which is recorded as the first function.

[0163] The fuel quantity and speed cross sequence calculation module is connected to the first function fitting module and is used to calculate the fuel quantity and speed cross sequence according to the aircraft fuel quantity change data sequence, the aircraft average speed data sequence and the first function; the calculation formula of the fuel quantity and speed cross sequence is:

[0164] ;

[0165] in, is the first in the oil speed cross sequence individual data; represents the first function.

[0166] The extreme value calculation module is connected to the oil quantity and speed cross sequence calculation module to search for the maximum value in the oil quantity and speed cross sequence, which is recorded as the first maximum value. ; Search for the minimum value in the oil volume and speed cross sequence, recorded as the first minimum value ; Search for the maximum value in the aircraft speed change data sequence, record it as the second maximum value ; Search for the minimum value in the aircraft speed change data sequence, recorded as the second minimum ; Search for the maximum value in the aircraft average speed data sequence, recorded as the third maximum value ; Search for the minimum value in the aircraft average speed data sequence, recorded as the third minimum ; Search for the maximum value in the aircraft trajectory length change data sequence, recorded as the fourth maximum value ; Search for the minimum value in the data sequence of the change in the aircraft trajectory length, recorded as the fourth minimum .

[0167] The normalization module is connected to the extreme value calculation module, and is used to calculate the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence according to the first maximum value, the first minimum value, the second maximum value, the second minimum value, the third maximum value, the third minimum value, the fourth maximum value, the fourth minimum value, the aircraft trajectory length change data sequence, the aircraft speed change data sequence, the aircraft average speed data sequence, and the fuel speed cross sequence using a normalization formula; the normalization formula is:

[0168] ;

[0169] in, is the first sequence in the first alignment individual data; is the first sequence in the second alignment individual data; is the first individual data; is the fourth aligned sequence data.

[0170] A comparison feature algorithm module is connected to the normalization module and is used to calculate the comparison mark according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence using the comparison feature algorithm. .

[0171] Furthermore, the comparison feature algorithm module includes:

[0172] A comparison calculation module, used to calculate the comparison factor according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence ; The calculation formula of the comparison factor is:

[0173] ;

[0174] in, is the first mean, is the second mean, is the third mean, is the fourth mean, is the first factor, is the second factor.

[0175] The comparison mark calculation module is connected to the comparison calculation module and is used to calculate the comparison mark according to the comparison factor; the calculation formula of the comparison mark is:

[0176] ;

[0177] in, is a preset comparison threshold.

[0178] Furthermore, the identification module includes:

[0179] The identification output module is used to identify the correctness of the VR model of the aerial vehicle according to the value of the comparison mark. If the value of the comparison mark is 1, "the VR model is correct" is output to the training personnel through the human-machine interface; if the value of the comparison mark is 0, "the VR model is wrong" is output to the training personnel through the human-machine interface.

[0180] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0181] Finally, it should be noted that: Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. 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 method for identifying aerial vehicles based on VR, characterized in that: The method comprises the following steps: S1, obtaining first data; the first data includes a preset simulation time period and a data sampling time interval; S2, reading the simulation results of the VR model of the aerial aircraft obtained by pre-simulation; extracting data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval, and obtaining a data sequence of the remaining fuel volume of the aircraft, a data sequence of the aircraft speed, and a data sequence of the aircraft trajectory length; S3, using a flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence to obtain a comparison mark; S4, identifying the correctness of the VR model of the aerial vehicle according to the comparison mark, and outputting the identification result to the training personnel; The method of using the flight data comparison algorithm to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence to obtain the comparison mark includes: The aircraft fuel quantity change data sequence is calculated based on the aircraft remaining fuel quantity data sequence; the lengths of the aircraft remaining fuel quantity data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence are equal, which is recorded as the first data length The calculation formula of the aircraft fuel quantity change data sequence is: ； in, Indicates the data number, which can be a value from 1 to integer variable of ; The number of aircraft fuel quantity change data series Data, in kilograms; The number of remaining fuel in the aircraft data series individual data; The number of remaining fuel in the aircraft data series individual data; The aircraft trajectory length change data sequence is calculated based on the aircraft trajectory length data sequence; the calculation formula of the aircraft trajectory length change data sequence is: ； in, is the first Data, in meters; is the first individual data; is the first individual data; The aircraft speed change data sequence is calculated based on the aircraft speed data sequence; the calculation formula of the aircraft speed change data sequence is: ； in, is the first Data, in meters per second; is the first individual data; is the first individual data; The aircraft average speed data sequence is calculated based on the aircraft speed data sequence; the calculation formula of the aircraft average speed data sequence is: ； in, is the number in the aircraft average speed data series Data, in meters per second; Read the pre-collected aircraft output power samples and aircraft fuel quantity change samples; according to the aircraft output power samples and aircraft fuel quantity change samples, a nonlinear fitting algorithm is used to fit the functional relationship between the aircraft output power and the aircraft fuel quantity change, which is recorded as a first function; The fuel quantity and speed cross sequence is calculated based on the aircraft fuel quantity change data sequence, the aircraft average speed data sequence and the first function; the calculation formula of the fuel quantity and speed cross sequence is: ； in, is the first in the oil speed cross sequence individual data; represents the first function; Search for the maximum value in the oil volume and speed cross sequence, and record it as the first maximum value ; Search for the minimum value in the oil volume and speed cross sequence, recorded as the first minimum value ; Search for the maximum value in the aircraft speed change data sequence, record it as the second maximum value ; Search for the minimum value in the aircraft speed change data sequence, recorded as the second minimum ; Search for the maximum value in the aircraft average speed data sequence, recorded as the third maximum value ; Search for the minimum value in the aircraft average speed data sequence, recorded as the third minimum ; Search for the maximum value in the aircraft trajectory length change data sequence, recorded as the fourth maximum value ; Search for the minimum value in the data sequence of the change in the aircraft trajectory length, recorded as the fourth minimum ; According to the first maximum value, the first minimum value, the second maximum value, the second minimum value, the third maximum value, the third minimum value, the fourth maximum value, the fourth minimum value, the aircraft trajectory length change data sequence, the aircraft speed change data sequence, the aircraft average speed data sequence, and the fuel speed cross sequence, a normalization formula is used to calculate the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence; the normalization formula is: ； in, is the first sequence in the first alignment individual data; is the first sequence in the second alignment individual data; is the third aligned sequence individual data; is the fourth aligned sequence individual data; According to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence, a comparison mark is calculated using a comparison feature algorithm. ; The comparison feature algorithm is used to calculate the comparison mark according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence. The methods include: The alignment factor is calculated based on the first alignment sequence, the second alignment sequence, the third alignment sequence, and the fourth alignment sequence. ; The calculation formula of the comparison factor is: ； in, is the first mean, is the second mean, is the third mean, is the fourth mean, is the first factor, is the second factor; The comparison mark is calculated according to the comparison factor; the calculation formula of the comparison mark is: ； in, is a preset comparison threshold.

2. The method for identifying aerial vehicles based on VR as claimed in claim 1, characterized in that: The method of extracting data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval to obtain the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence includes: Read the change curve of aircraft fuel tank weight over time, the change curve of aircraft speed over time, and the change curve of aircraft flight track length over time from the simulation results of the aircraft VR model in the air; According to the data sampling time interval, the values ​​of the curve of the change of the aircraft tank weight over time are sampled to obtain the aircraft remaining fuel data sequence; according to the data sampling time interval, the values ​​of the curve of the change of the aircraft speed over time are sampled to obtain the aircraft speed data sequence; according to the data sampling time interval, the values ​​of the curve of the change of the aircraft flight trajectory length over time are sampled to obtain the aircraft trajectory length data sequence.

3. The method for identifying aerial vehicles based on VR as claimed in claim 2, characterized in that: The method of identifying the correctness of the VR model of the aerial vehicle according to the comparison mark and outputting the identification result to the training personnel includes: The correctness of the VR model of the aerial vehicle is identified based on the value of the comparison mark. If the value of the comparison mark is 1, "VR model is correct" is output to the training personnel through the human-machine interface. If the value of the comparison mark is 0, "VR model is wrong" is output to the training personnel through the human-machine interface.

4. The VR-based aerial vehicle identification system is characterized by: The system comprises: A first data reading module, used to obtain first data; the first data includes a preset simulation time period and a data sampling time interval; The simulation data reading module is connected to the first data reading module and is used to read the simulation results of the VR model of the aerial aircraft obtained by pre-simulation; extract data from the simulation results of the VR model of the aerial aircraft within the simulation time period according to the data sampling time interval to obtain the data sequence of the remaining fuel volume of the aircraft, the data sequence of the aircraft speed, and the data sequence of the aircraft trajectory length; A comparison calculation module is connected to the simulation data reading module and is used to compare and calculate the aircraft remaining fuel data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence using a flight data comparison algorithm to obtain a comparison mark; The recognition module is connected to the comparison calculation module and is used to identify the correctness of the VR model of the aerial vehicle according to the comparison mark and output the recognition result to the training personnel; The comparison calculation module comprises: The aircraft fuel quantity change data sequence calculation module is used to calculate the aircraft fuel quantity change data sequence according to the aircraft remaining fuel quantity data sequence; the lengths of the aircraft remaining fuel quantity data sequence, the aircraft speed data sequence, and the aircraft trajectory length data sequence are equal, which is recorded as the first data length The calculation formula of the aircraft fuel quantity change data sequence is: ； in, Indicates the data number, which can be a value from 1 to integer variable of ; The number of aircraft fuel quantity change data series Data, in kilograms; The number of remaining fuel in the aircraft data series individual data; The number of remaining fuel in the aircraft data series individual data; The aircraft trajectory length change data sequence calculation module is connected to the aircraft fuel quantity change data sequence calculation module and is used to calculate the aircraft trajectory length change data sequence according to the aircraft trajectory length data sequence; the calculation formula of the aircraft trajectory length change data sequence is: ； in, is the first Data, in meters; is the first individual data; is the first individual data; The aircraft speed change data sequence calculation module is connected to the aircraft trajectory length change data sequence calculation module and is used to calculate the aircraft speed change data sequence according to the aircraft speed data sequence; the calculation formula of the aircraft speed change data sequence is: ； in, is the first Data, in meters per second; is the first individual data; is the first individual data; The aircraft average speed data sequence calculation module is connected to the aircraft speed change data sequence calculation module and is used to calculate the aircraft average speed data sequence according to the aircraft speed data sequence; the calculation formula of the aircraft average speed data sequence is: ； in, is the number in the aircraft average speed data series Data, in meters per second; A first function fitting module is connected to the aircraft average speed data sequence calculation module and is used to read the aircraft output power samples and aircraft fuel quantity change samples collected in advance; according to the aircraft output power samples and aircraft fuel quantity change samples, a nonlinear fitting algorithm is used to fit the functional relationship between the aircraft output power and the aircraft fuel quantity change, which is recorded as a first function; The fuel quantity and speed cross sequence calculation module is connected to the first function fitting module and is used to calculate the fuel quantity and speed cross sequence according to the aircraft fuel quantity change data sequence, the aircraft average speed data sequence and the first function; the calculation formula of the fuel quantity and speed cross sequence is: ； in, is the first in the oil speed cross sequence individual data; represents the first function; The extreme value calculation module is connected to the oil quantity and speed cross sequence calculation module to search for the maximum value in the oil quantity and speed cross sequence, which is recorded as the first maximum value. ; Search for the minimum value in the oil volume and speed cross sequence, recorded as the first minimum value ; Search for the maximum value in the aircraft speed change data sequence, record it as the second maximum value ; Search for the minimum value in the aircraft speed change data sequence, recorded as the second minimum ; Search for the maximum value in the aircraft average speed data sequence, recorded as the third maximum value ; Search for the minimum value in the aircraft average speed data sequence, recorded as the third minimum ; Search for the maximum value in the aircraft trajectory length change data sequence, recorded as the fourth maximum value ; Search for the minimum value in the data sequence of the change in the aircraft trajectory length, recorded as the fourth minimum ; The normalization module is connected to the extreme value calculation module, and is used to calculate the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence according to the first maximum value, the first minimum value, the second maximum value, the second minimum value, the third maximum value, the third minimum value, the fourth maximum value, the fourth minimum value, the aircraft trajectory length change data sequence, the aircraft speed change data sequence, the aircraft average speed data sequence, and the fuel speed cross sequence using a normalization formula; the normalization formula is: ； in, is the first sequence in the first alignment individual data; is the first sequence in the second alignment individual data; is the third aligned sequence individual data; is the fourth aligned sequence individual data; A comparison feature algorithm module is connected to the normalization module and is used to calculate the comparison mark according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence using the comparison feature algorithm. ; The comparison feature algorithm module includes: A comparison calculation module, used to calculate the comparison factor according to the first comparison sequence, the second comparison sequence, the third comparison sequence, and the fourth comparison sequence ; The calculation formula of the comparison factor is: ； in, is the first mean, is the second mean, is the third mean, is the fourth mean, is the first factor, is the second factor; The comparison mark calculation module is connected to the comparison calculation module and is used to calculate the comparison mark according to the comparison factor; the calculation formula of the comparison mark is: ； in, is a preset comparison threshold.

5. The VR-based aerial vehicle identification system according to claim 4, characterized in that: The simulation data reading module comprises: The curve reading module is used to read the change curve of the aircraft tank weight over time, the change curve of the aircraft speed over time, and the change curve of the aircraft flight track length over time from the simulation results of the aircraft VR model in the air; The data sampling module is connected to the curve reading module and is used to sample the values ​​of the aircraft tank weight change curve over time according to the data sampling time interval to obtain the aircraft remaining fuel data sequence; sample the values ​​of the aircraft speed change curve over time according to the data sampling time interval to obtain the aircraft speed data sequence; sample the values ​​of the aircraft trajectory length change curve over time according to the data sampling time interval to obtain the aircraft trajectory length data sequence.

6. The VR-based aerial vehicle identification system according to claim 5, characterized in that: The identification module comprises: The identification output module is used to identify the correctness of the VR model of the aerial vehicle according to the value of the comparison mark. If the value of the comparison mark is 1, "VR model is correct" is output to the training personnel through the human-machine interface; if the value of the comparison mark is 0, "VR model is wrong" is output to the training personnel through the human-machine interface.

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