CN117786375A - Method for evaluating control quality of guiding following flight in instrument flight mode - Google Patents
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Abstract
The invention relates to the technical field of flight control, in particular to a method for evaluating the quality of following flight control under an instrument flight mode, which comprises the following steps: step 1, acquiring and preprocessing QAR data of a following flight sample; step 2, guiding the similarity measurement of the following flight attitude curve; firstly, an aircraft target attitude curve is obtained according to FD attitude parameters, and four similarity indexes of the aircraft target attitude curve and an aircraft real-time attitude curve are calculated, wherein the four curve similarity indexes are as follows: general trend, time difference, numerical difference, data span; secondly, determining similarity index weights by using an entropy weight method; finally, fusing similarity indexes and weights thereof to obtain a gesture curve similarity result; step 3, constructing a wind field stability grading model based on wind change rate; and 4, grading the following flight control quality. The invention can better evaluate the control quality of following the flight by guiding.
Description
Technical Field
The invention relates to the technical field of flight control, in particular to a method for evaluating the quality of following flight control under the instrument flight mode.
Background
The automatic Flight control system of the aircraft can effectively reduce the workload of pilots and improve the Flight safety and efficiency, and the main working principle is that a Flight Guidance computer (FG) receives real-time data of each Flight parameter, calculates and outputs an aircraft target attitude Guidance instruction according to a mode control rate set by the pilots, and an autopilot operates the aircraft according to the instruction in an autopilot mode; in manual pilot mode, the attitude guidance command is displayed on a main flight display (Primary Flight Display, PFD) in the form of FD command sticks, and an operator (pilot or autopilot) maneuvers the aircraft by following the guideline to achieve the target trajectory. Thus, maintaining accurate and timely follow of guidance is an important indicator of the quality of the maneuver of the airborne automatic flight system, as well as a necessary observable behavioral indicator (Observable behavior, OB) that measures pilot's manual maneuver competence (FPM). In particular, in view of the urgent need for improving airspace utilization by civil aviation, there is a need for pilots/autopilots to be able to follow the flight guidance command signals to precisely control the attitude of the aircraft, to ensure that the aircraft is in the desired flight path, such as by performing RNP AR operations at complex terrain airports, where the aircraft along-the-flight error must not exceed ± 0.1 seas (RNP 0.1) for at least 95% of the total flight time.
Currently, research literature and patents related to flight control quality assessment methods are focused on the aspect of overrun event monitoring, and patents related to flight guidance are focused on the display technology of flight guidance information. In the case of modern aircraft operation where autopilot and intelligent assistance techniques (i.e., flight guidance) are commonly employed, there is little research into incorporating accuracy of maneuvering following flight guidance into flight maneuvering quality assessment. Therefore, a method for quantitatively evaluating the control quality during guiding following flight based on flight parameter data is necessary to design, and the method has great significance in improving the manual flight capacity of pilots, optimizing the design of an automatic driving system and further improving the airspace utilization rate.
Disclosure of Invention
The invention provides a method for evaluating the quality of guiding following flight control in an instrument flight mode, which can evaluate the quality of guiding following flight control better.
According to the invention, the method for evaluating the control quality of the following flight in the instrument flight mode comprises the following steps:
step 1, acquiring and preprocessing QAR data of a following flight sample;
collecting and guiding QAR data of a following flight sample, preprocessing, judging an abnormal value with deviation larger than a boundary value by adopting a box graph, filling the missing value or replacing the abnormal value by using an average value of adjacent data, and obtaining a time sequence curve data set of an evaluation index;
step 2, guiding the similarity measurement of the following flight attitude curve;
firstly, an aircraft target attitude curve is obtained according to FD attitude parameters, and four similarity indexes of the aircraft target attitude curve and an aircraft real-time attitude curve are calculated, wherein the four curve similarity indexes are as follows: general trend, time difference, numerical difference, data span; secondly, determining similarity index weights by using an entropy weight method; finally, fusing similarity indexes and weights thereof to obtain a gesture curve similarity result;
step 3, constructing a wind field stability grading model based on wind change rate;
obtaining a wind change rate index representing the stability of the wind field by utilizing the time sequence derivation of the wind direction and the wind speed recorded in the QAR data, constructing a wind change rate data set, calculating the standard deviation of the wind change rate and counting the distribution characteristics of the standard deviation, and constructing a wind field stability grading model based on the wind change rate;
step 4, grading the following flight control quality;
mapping the similarity of the gesture curves of the guide following flight samples determined in the step 2 with the wind field grade of the samples obtained in the step 3 to obtain gesture curve similarity data sets under the stability grade of each wind field, determining a control quality rating threshold value based on the data sets, and constructing guide following flight control quality rating models under different wind field stability grades.
Preferably, in step 2, the method specifically comprises the following steps:
2.1. calculating a pitch/roll gesture curve similarity index;
determining a pitching/rolling gesture curve of an airplane target according to the FD pitching/rolling gesture parameters, carrying out similarity measurement with the real-time pitching/rolling gesture curve of the airplane, and designing a calculation model of four curve similarity indexes;
2.2. determining index weight by an entropy weight method;
calculating pitch gesture curve similarity indexes and roll gesture curve similarity indexes for the sample data acquired in the step 1, constructing a data set of each index, calculating information entropy according to each index data set, and determining each index weight by using an entropy weight method;
2.3. guiding the similarity of the following flight attitude curves;
and (3) calculating the similarity of the curve of the guide following flight attitude according to the similarity index of the curve of the pitch/roll attitude determined in the step (2.1) and the weight coefficient of each index determined in the step (2.2).
Preferably, in step 2.1, specifically:
2.1.1 Overall trend)
Accumulating absolute values of differences between two curves of each time sequence, and then calculating the average value of the absolute values to measure trend differences of the curves; the method for measuring the unidirectional distance is introduced from the space shape, and comprises the following specific calculation steps:
(i) Real-time attitude curve of airplaneC RT To aircraft target attitude profileC FD The formula is as follows:
；
wherein:pis a real-time attitude curve of the airplaneC RT In (c) is a point of the matrix,qis the attitude curve of the airplane targetC FD Is a dot in (2);is a dotpTo the pointqEuropean distance,/, of->For real-time attitude curve of aircraftC RT The number of data points, ">Is a unidirectional distance;
(ii) General trend between two curvesI.e. +.>And->The average distance of (2) is as follows:
；
2.1.2 Time difference)
The time difference is defined as two time series curvesmA time difference average value between feature points in the same dimension; firstly, obtaining the maximum values of an airplane real-time attitude curve and an airplane target attitude curve, dividing each curve into a plurality of monotonic intervals according to the maximum values, and setting the minimum value as an initial characteristic point pair for the monotonic increasing interval; calculating the next characteristic point pair according to the type of the initial characteristic point pair in each interval, taking the difference value as time difference, sequentially calculating a difference set of each time period, and averaging to obtain the time differenceThe formula is as follows:
；
wherein:the number of the feature points; />The number of the monotone intervals; />Points for each monotonic interval; />Is the target attitude of the aircraft; />The real-time attitude of the aircraft is realized; />In order to make the plane target attitude curve +.>The individual section posture data is +.>The abscissa of the large value;
2.1.3 Numerical difference)
The numerical difference is defined as a state that the translated curve and the aircraft target attitude curve are most similar by translating the aircraft real-time attitude curve; numerical differenceThe formula of (2) is as follows:
；
wherein:and->Respectively acquiring longitudinal coordinate values of current data of an aircraft target attitude curve and an aircraft real-time attitude curve;Lthe time length of the gesture data curve;
2.1.4 Data span)
The overall data span of the curve reflects the stability of the flight attitude control, namely, the data span of the real-time attitude of the aircraft is within the data span of the target attitude of the aircraft, and if the data span of the real-time attitude of the aircraft is within the data span of the target attitude of the aircraft, the control is stable, otherwise, the control is unstable, and the risk of losing control exists; the data span of the whole can be expressed by using the maximum difference, and the maximum difference of the ordinate is calculated as the data span of 2 curvesThe formula is as follows:
；
in the method, in the process of the invention,respectively the maximum value and the minimum value of the ordinate of the aircraft target attitude curve; />The maximum value and the minimum value of the ordinate of the real-time attitude curve of the aircraft are respectively.
Preferably, in step 2.2), specifically:
2.2.1 Step 2.1) the inclusion is obtainedSample number->Data set of individual attribute metrics +.>Calculate +.>The individual attribute measures account for->Specific gravity of individual samples->The formula is as follows:
；
2.2.2 Calculating the firstEntropy value of individual attribute measure->The formula is as follows:
；
2.2.3 Calculating the firstWeight coefficient of individual attribute->The formula is as follows:
。
preferably, in step 2.3), specifically:
obtaining pitch attitude curve similarity measurementRoll gesture curve similarity measure +.>The calculation formula of (2) is as follows:
；
in the method, in the process of the invention,the method comprises the steps of respectively obtaining a general trend similarity index, a time difference similarity index, a numerical value difference similarity index and a telescopic similarity index of a pitching gesture curve;weights of 4 pitch attitude similarity indexes respectively;the overall trend similarity index, the time difference similarity index, the numerical value difference similarity index and the telescopic similarity index of the roll gesture curve are respectively; />Weights of 4 roll gesture similarity indexes respectively;
finally, the similarity of the gesture curves of the guide following flight samples can be obtainedThe formula is as follows:
。
preferably, in step 3, specifically:
3.1 Calculating the wind change rateThe formula is as follows:
；
wherein:is the wind speed; />Is the wind direction; />Is the current moment; />Is the previous time;
using standard deviation of wind variation rateAs an index for measuring the stability of a wind field over a period of time, the formula is as follows:
；
wherein:Lis the time sequence length;is the average value of the wind change rate;
3.2 Wind farm classification model):
according toIs used for determining wind field classification>The formula is as follows
；
The wind farm is divided into four classes,not more than->When the current environment is regarded as stable wind, the current environment is recorded as level 1;in the range of->When the current environment is regarded as slight turbulence, the current environment is marked as grade 2; />In the range of->When the current environment is regarded as moderate turbulence, the current environment is marked as level 3; />Exceed->When the current environment is regarded as serious unstable wind, the current environment is marked as grade 4; />For the classification threshold, satisfy +.>。
Preferably, in step 4, specifically:
mapping the scores and the wind field stability grades to obtain score data sets under the wind field stability of each grade, dividing the score data sets into 5 operation quality grades based on data set distribution, and obtaining operation quality rating models under different wind field stability conditions, wherein the formula is as follows:
；
wherein:is the wind field stability class,/->Is corresponding to wind field stability->A flight maneuver quality classification threshold at stage; when the wind field stability is +.>In the stage of stage, the head is>Exceed->Indicating that the guiding following operation of the flight is excellent and marked as 5 points; when->In the range, the guiding following operation of the flight is shown to perform well, and the score is recorded as 4; />In the range, the guiding following operation performance of the flight is indicated to be medium and is recorded as 3 points; />Within the range, the guiding following operation performance of the flight meets the minimum requirement, and is recorded as 2 points; />Indicating that the following maneuver for this flight performed poorly, recorded as 1 minute.
The beneficial effects of the invention are as follows:
(1) The quantitative rating of the steering following flight operation accuracy is realized through the similarity analysis of the real-time attitude parameter (pitch angle and roll angle) curve of the airplane and the target attitude curve of the airplane obtained by the FD attitude parameters (FD pitch angle and FD roll angle);
(2) In the manual driving mode, an operator to be evaluated is a pilot, a method for quantitatively evaluating the manual control of the attitude precision degree of the pilot for airlines, training institutions and the like can be provided, and the manual control capability of the pilot in the training plan and instrument flight mode is improved in a targeted manner;
(3) In the automatic driving mode, an operator to be evaluated is an automatic pilot, and through quantitative evaluation of the following flight control quality under different meteorological conditions, avionics equipment manufacturers can be helped to comprehensively evaluate the accuracy of the automatic pilot equipment on flight attitude control, and the design of an automatic pilot system is optimized.
Drawings
FIG. 1 is a flow chart of a method for evaluating quality of pilot following flight maneuvers in an instrumented flight mode;
FIG. 2 is a flow chart of similarity measurement for guiding a following flight attitude curve in an embodiment.
Detailed Description
For a further understanding of the present invention, the present invention will be described in detail with reference to the drawings and examples. It is to be understood that the examples are illustrative of the present invention and are not intended to be limiting.
Examples
As shown in fig. 1, the present embodiment provides a method for evaluating the steering quality of following a flight in an instrument flight mode, which includes the following steps:
step 1, acquiring and preprocessing QAR data of a following flight sample;
based on flight expert experience and the maneuvering characteristics of FD following flight, QAR data samples are collected that direct following flight over a period of time, as shown in table 1.
And judging an abnormal value with the deviation larger than the boundary value by adopting the box graph, filling the missing value or replacing the abnormal value by using the average value of the adjacent data, and obtaining a time sequence curve data set of the evaluation index.
Step 2, guiding the similarity measurement of the following flight attitude curve;
the accuracy of guiding following flight control is evaluated by comparing the similarity between the aircraft target attitude curve and the aircraft real-time attitude curve, so that an evaluation method based on the curve similarity theory is designed, and similarity measurement is carried out on the aircraft real-time attitude curve and the aircraft target attitude curve. The abscissa of the curve is the data acquisition time, and the ordinate is the attitude data. Firstly, determining an aircraft target attitude curve according to FD attitude parameters, and calculating four similarity indexes of the aircraft target attitude curve and an aircraft real-time attitude curve, wherein the four similarity indexes are as follows: general trend, time difference, numerical difference, data span; secondly, determining similarity index weights by using an entropy weight method; finally, the similarity indexes and the weights thereof are fused to obtain a gesture curve similarity result, as shown in fig. 2. The method comprises the following steps:
2.1. calculating a pitch/roll gesture curve similarity index;
obtaining a pitching/rolling gesture curve of an airplane target according to the FD pitching/rolling gesture parameters, carrying out similarity measurement with the real-time pitching/rolling gesture curve of the airplane, and designing a calculation model of four curve similarity indexes;
2.1.1 Overall trend)
Accumulating absolute values of differences between two curves of each time sequence, and then calculating the average value of the absolute values to measure trend differences of the curves; the method for measuring the unidirectional distance is introduced from the space shape, and comprises the following specific calculation steps:
(i) Real-time attitude curve of airplaneC RT To aircraft target attitude profileC FD The formula is as follows:
；
wherein:pis a real-time attitude curve of the airplaneC RT In (c) is a point of the matrix,qis the attitude curve of the airplane targetC FD Is a dot in (2);is a dotpTo the pointqEuropean distance,/, of->For real-time attitude curve of aircraftC RT The number of data points, ">Is a unidirectional distance (i.e.)>Typically unequal).
(ii) General trend between two curvesI.e. +.>And->The average distance of (2) is as follows:
；
2.1.2 Time difference)
The time difference is defined as two time series curvesmA time difference average value between feature points in the same dimension; firstly, obtaining the maximum values of an airplane real-time attitude curve and an airplane target attitude curve, dividing each curve into a plurality of monotonic intervals according to the maximum values, and setting the minimum value as an initial characteristic point pair for the monotonic increasing interval; calculating the next characteristic point pair according to the type of the initial characteristic point pair in each interval, taking the difference value as time difference, sequentially calculating a difference set of each time period, and averaging to obtain the time differenceThe formula is as follows:
；
wherein:the number of the feature points; />The number of the monotone intervals; />Points for each monotonic interval; />Is the target attitude of the aircraft; />The real-time attitude of the aircraft is realized; />In order to make the plane target attitude curve +.>The individual section posture data is +.>The abscissa of the large value;
2.1.3 Numerical difference)
The numerical difference is defined as that the translated curve and the plane target attitude curve reach the most similar state by translating the plane real-time attitude curve, so that the accuracy of operation is reflected, and whether the plane attitude can be accurately regulated along with the FD guide can be accurately regulated. The smaller the numerical difference between the curves, the more precise the manipulation. Numerical differenceThe formula of (2) is as follows:
；
wherein:and->Respectively acquiring longitudinal coordinate values of current data of an aircraft target attitude curve and an aircraft real-time attitude curve;Lthe time length of the gesture data curve;
2.1.4 Data span)
The integral data span of the curve reflects the stability of the flight attitude control, namely, the control is stable when the real-time attitude data span of the aircraft is within the target attitude data span of the aircraftOn the contrary, the manipulation is unstable, and there is a risk of losing control. Considering that the selected time series are equally long on the abscissa, there is only a span of data on the ordinate. By means of the idea of dispersion normalization, the full data span can be fully represented by the maximum value differences. Calculating the difference between the ordinate and the ordinate as the data span of 2 curvesThe formula is as follows:
；
in the method, in the process of the invention,respectively the maximum value and the minimum value of the ordinate of the aircraft target attitude curve; />The maximum value and the minimum value of the ordinate of the real-time attitude curve of the aircraft are respectively.
2.2. Determining index weight by an entropy weight method;
calculating pitch gesture curve similarity indexes and roll gesture curve similarity indexes for the sample data acquired in the step 1, constructing a data set of each index, calculating information entropy according to each index data set, and determining each index weight by using an entropy weight method; the method comprises the following steps:
2.2.1 Step 2.1) the inclusion is obtainedSample number->Data set of individual attribute metrics +.>Calculate +.>The individual attribute measures account for->Specific gravity of individual samples->The formula is as follows:
；
2.2.2 Calculating the firstEntropy value of individual attribute measure->The formula is as follows:
；
2.2.3 Calculating the firstWeight coefficient of individual attribute->The formula is as follows:
。
2.3. guiding the similarity of the following flight attitude curves;
calculating the similarity of the curve of the guide following flight attitude according to the similarity index of the curve of the pitch/roll attitude determined in the step 2.1 and the weight coefficient of each index determined in the step 2.2;
obtaining pitch attitude curve similarity measurementRoll gesture curve similarity measure +.>The calculation formula of (2) is as follows:
；
in the method, in the process of the invention,the method comprises the steps of respectively obtaining a general trend similarity index, a time difference similarity index, a numerical value difference similarity index and a telescopic similarity index of a pitching gesture curve;weights of 4 pitch attitude similarity indexes respectively;the overall trend similarity index, the time difference similarity index, the numerical value difference similarity index and the telescopic similarity index of the roll gesture curve are respectively; />Weights of 4 roll gesture similarity indexes respectively;
finally, the similarity of the gesture curves of the guide following flight samples can be obtainedThe formula is as follows:
。
step 3, constructing a wind field stability grading model based on wind change rate;
wind field stability is a key factor affecting the control quality, and is divided into four stages of stable wind, slight turbulence, moderate turbulence and severe turbulence. Obtaining a wind change rate index representing the stability of the wind field by utilizing the wind direction and wind speed time sequence derivation recorded in the QAR data, calculating the standard deviation of the wind change rate by utilizing the collected sample data, constructing a data set, and constructing a wind field stability grading model based on the wind change rate by referring to the experience of flight experts. The method comprises the following steps:
3.1 Calculating the wind change rateThe formula is as follows:
；
wherein:is the wind speed; />Is the wind direction; />Is the current moment; />Is the previous time;
using standard deviation of wind variation rateAs an index for measuring the stability of a wind field over a period of time, the formula is as follows:
；
wherein:Lis the time sequence length;is the average value of the wind change rate;
3.2 Wind farm classification model):
according toIs used for determining wind field classification>The formula is as follows;
；
the wind farm is divided into four classes,not more than->When the current environment is regarded as stable wind, the current environment is recorded as level 1;in the range of->When the current environment is regarded as slight turbulence, the current environment is marked as grade 2; />In the range of->When the current environment is regarded as moderate turbulence, the current environment is marked as level 3; />Exceed->When the current environment is regarded as serious unstable wind, the current environment is marked as grade 4; />For the classification threshold, satisfy +.>。
Step 4, grading the following flight control quality;
the wind field stability is a key factor affecting the operation quality, so that the operation quality evaluation needs to consider the current wind field stability level, and design a corresponding rating threshold according to the operation difficulty under each wind field stability level. Mapping the similarity of the gesture curves of the guide following flight samples determined in the step 2 with the wind field grade of the samples obtained in the step 3 to obtain gesture curve similarity data sets under the stability grade of each wind field, determining a control quality rating threshold value based on the data sets, and constructing guide following flight control quality rating models under different wind field stability grades. The method comprises the following steps:
mapping the scores and the wind field stability grades to obtain score data sets under the wind field stability of each grade, dividing the score data sets into 5 operation quality grades based on data set distribution, and obtaining operation quality rating models under different wind field stability conditions, wherein the formula is as follows:
；
wherein:is the wind field stability class,/->Is corresponding to wind field stability->A flight maneuver quality classification threshold at stage; when the wind field stability is +.>In the stage of stage, the head is>Exceed->Indicating that the guiding following operation of the flight is excellent and marked as 5 points; when->In the range, the guiding following operation of the flight is shown to perform well, and the score is recorded as 4; />In the range, the guiding following operation performance of the flight is indicated to be medium and is recorded as 3 points; />Within the range, the guiding following operation performance of the flight meets the minimum requirement, and is recorded as 2 points; />Indicating that the following maneuver for this flight performed poorly, recorded as 1 minute.
The embodiment provides a method for quantitatively evaluating the capability of an operator to follow flight guidance information in an instrument flight mode and precisely control the pitching and rolling postures of an airplane, and an evaluation object is suitable for pilots and autopilot systems. For the manual driving mode, a new dimension can be provided for airlines, training institutions and the like to evaluate the manual control flight quality of pilots, so that training plans are perfected pertinently, and the manual flight capacity of the pilots is comprehensively improved; for the autopilot mode, the accuracy of the autopilot system can be checked, facilitating its design optimization.
The invention and its embodiments have been described above by way of illustration and not limitation, and the invention is illustrated in the accompanying drawings and described in the drawings in which the actual structure is not limited thereto. Therefore, if one of ordinary skill in the art is informed by this disclosure, the structural mode and the embodiments similar to the technical scheme are not creatively designed without departing from the gist of the present invention.
Claims (7)
1. The method for evaluating the control quality of guiding following flight in the instrument flight mode is characterized by comprising the following steps of: the method comprises the following steps:
step 1, acquiring and preprocessing QAR data of a following flight sample;
collecting and guiding QAR data of a following flight sample, preprocessing, judging an abnormal value with deviation larger than a boundary value by adopting a box graph, filling the missing value or replacing the abnormal value by using an average value of adjacent data, and obtaining a time sequence curve data set of an evaluation index;
step 2, guiding the similarity measurement of the following flight attitude curve;
firstly, an aircraft target attitude curve is obtained according to FD attitude parameters, and four similarity indexes of the aircraft target attitude curve and an aircraft real-time attitude curve are calculated, wherein the four curve similarity indexes are as follows: general trend, time difference, numerical difference, data span; secondly, determining similarity index weights by using an entropy weight method; finally, fusing similarity indexes and weights thereof to obtain a gesture curve similarity result;
step 3, constructing a wind field stability grading model based on wind change rate;
obtaining a wind change rate index representing the stability of the wind field by utilizing the time sequence derivation of the wind direction and the wind speed recorded in the QAR data, constructing a wind change rate data set, calculating the standard deviation of the wind change rate and counting the distribution characteristics of the standard deviation, and constructing a wind field stability grading model based on the wind change rate;
step 4, grading the following flight control quality;
mapping the similarity of the gesture curves of the guide following flight samples determined in the step 2 with the wind field grade of the samples obtained in the step 3 to obtain gesture curve similarity data sets under the stability grade of each wind field, determining a control quality rating threshold value based on the data sets, and constructing guide following flight control quality rating models under different wind field stability grades.
2. The method for evaluating the quality of a pilot-following flight maneuver in an instrumented flight mode as defined in claim 1 wherein: the step 2 specifically comprises the following steps:
2.1. calculating a pitch/roll gesture curve similarity index;
obtaining a pitching/rolling gesture curve of an airplane target according to the FD pitching/rolling gesture parameters, carrying out similarity measurement with the real-time pitching/rolling gesture curve of the airplane, and designing a calculation model of four curve similarity indexes;
2.2. determining index weight by an entropy weight method;
calculating pitch gesture curve similarity indexes and roll gesture curve similarity indexes for the sample data acquired in the step 1, constructing a data set of each index, calculating information entropy according to each index data set, and determining each index weight by using an entropy weight method;
2.3. guiding the similarity of the following flight attitude curves;
and (3) calculating the similarity of the curve of the guide following flight attitude according to the similarity index of the curve of the pitch/roll attitude determined in the step (2.1) and the weight coefficient of each index determined in the step (2.2).
3. The method for evaluating the quality of a pilot-following flight maneuver in an instrumented flight mode as defined in claim 2 wherein: in step 2.1, specifically:
2.1.1 Overall trend)
Accumulating absolute values of differences between two curves of each time sequence, and then calculating the average value of the absolute values to measure trend differences of the curves; the method for measuring the unidirectional distance is introduced from the space shape, and comprises the following specific calculation steps:
(i) Real-time attitude curve of airplaneC RT To aircraft target attitude profileC FD The formula is as follows:
；
wherein:pis a real-time attitude curve of the airplaneC RT In (c) is a point of the matrix,qis the attitude curve of the airplane targetC FD Is a dot in (2);is a dotpTo the pointqEuropean distance,/, of->For real-time attitude curve of aircraftC RT The number of data points, ">Is a unidirectional distance;
(ii) General trend between two curvesI.e. +.>And->The average distance of (2) is as follows:
；
2.1.2 Time difference)
The time difference is defined as two time series curvesmA time difference average value between feature points in the same dimension; firstly, obtaining the maximum values of an airplane real-time attitude curve and an airplane target attitude curve, dividing each curve into a plurality of monotonic intervals according to the maximum values, and setting the minimum value as an initial characteristic point pair for the monotonic increasing interval; calculating the next characteristic point pair according to the type of the initial characteristic point pair in each interval, taking the difference value as time difference, sequentially calculating a difference set of each time period, and averaging to obtain the time differenceThe formula is as follows:
；
wherein:the number of the feature points; />The number of the monotone intervals; />Points for each monotonic interval; />Is the target attitude of the aircraft; />The real-time attitude of the aircraft is realized; />In order to make the plane target attitude curve +.>The individual section posture data is +.>The abscissa of the large value;
2.1.3 Numerical difference)
The numerical difference is defined as a state that the translated curve and the aircraft target attitude curve are most similar by translating the aircraft real-time attitude curve; numerical differenceThe formula of (2) is as follows:
；
wherein:and->Respectively acquiring longitudinal coordinate values of current data of an aircraft target attitude curve and an aircraft real-time attitude curve;Lthe time length of the gesture data curve;
2.1.4 Data span)
The overall data span of the curve reflects the stability of the flight attitude control, namely, the data span of the real-time attitude of the aircraft is within the data span of the target attitude of the aircraft, and if the data span of the real-time attitude of the aircraft is within the data span of the target attitude of the aircraft, the control is stable, otherwise, the control is unstable, and the risk of losing control exists; by using the difference of the maximum valuesCan show the whole data span, calculate the maximum difference of the ordinate as the data span of 2 curvesThe formula is as follows:
；
in the method, in the process of the invention,respectively the maximum value and the minimum value of the ordinate of the aircraft target attitude curve; />The maximum value and the minimum value of the ordinate of the real-time attitude curve of the aircraft are respectively.
4. A method of estimating the quality of a pilot-following flight maneuver in an instrumented flight mode as defined in claim 3 wherein: in the step 2.2), the specific steps are as follows:
2.2.1 Step 2.1) the inclusion is obtainedSample number->Data set of individual attribute metrics +.>Calculate +.>The individual attribute measures account for->Specific gravity of individual samples->The formula is as follows:
；
2.2.2 Calculating the firstEntropy value of individual attribute measure->The formula is as follows:
；
2.2.3 Calculating the firstWeight coefficient of individual attribute->The formula is as follows:
。
5. the method for evaluating the quality of a pilot-following flight maneuver in an instrumented flight mode as defined in claim 4 wherein: in the step 2.3), the method specifically comprises the following steps:
obtaining pitch attitude curve similarity measurementRoll gesture curve similarity measure +.>The calculation formula of (2) is as follows:
；
in the method, in the process of the invention,the method comprises the steps of respectively obtaining a general trend similarity index, a time difference similarity index, a numerical value difference similarity index and a telescopic similarity index of a pitching gesture curve;weights of 4 pitch attitude similarity indexes respectively;the overall trend similarity index, the time difference similarity index, the numerical value difference similarity index and the telescopic similarity index of the roll gesture curve are respectively; />Weights of 4 roll gesture similarity indexes respectively;
finally, the similarity of the gesture curves of the guide following flight samples can be obtainedThe formula is as follows:
。
6. the method for evaluating the quality of a pilot-following flight maneuver in an instrumented flight mode as defined in claim 5 wherein: in step 3, specifically:
3.1 Calculating the wind change rateThe formula is as follows:
；
wherein:is the wind speed; />Is the wind direction; />Is the current moment; />Is the previous time;
using standard deviation of wind variation rateAs an index for measuring the stability of a wind field over a period of time, the formula is as follows:
；
wherein:Lis the time sequence length;is the average value of the wind change rate;
3.2 Wind farm classification model):
according toIs used for determining wind field classification>The formula is as follows
；
The wind farm is divided into four classes,not more than->When the current environment is regarded as stable wind, the current environment is recorded as level 1;in the range of->When the current environment is regarded as slight turbulence, the current environment is marked as grade 2; />In the range of->When the current environment is regarded as moderate turbulence, the current environment is marked as level 3; />Exceed->When the current environment is regarded as serious unstable wind, the current environment is marked as grade 4; />For the classification threshold, satisfy +.>。
7. The method for evaluating the quality of a pilot-following flight maneuver in an instrumented flight mode as defined in claim 6 wherein: in step 4, specifically:
mapping the scores and the wind field stability grades to obtain score data sets under the wind field stability of each grade, dividing the score data sets into 5 operation quality grades based on data set distribution, and obtaining operation quality rating models under different wind field stability conditions, wherein the formula is as follows:
；
wherein:is the wind field stability class,/->Is corresponding to wind field stability->A flight maneuver quality classification threshold at stage; when the wind field stability is +.>In the stage of stage, the head is>Exceed->Indicating that the guiding following operation of the flight is excellent and marked as 5 points; when->In the range, the guiding following operation of the flight is shown to perform well, and the score is recorded as 4; />In the range, the guiding following operation performance of the flight is indicated to be medium and is recorded as 3 points; />Within the range, the guiding following operation performance of the flight meets the minimum requirement, and is recorded as 2 points; />Watch (Table)The index following maneuver for this flight was clearly out of order, and was recorded as 1 point.
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