CN116010889A - An intelligent identification method for abnormal flight status of aviation aircraft - Google Patents

An intelligent identification method for abnormal flight status of aviation aircraft Download PDF

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CN116010889A
CN116010889A CN202211725975.1A CN202211725975A CN116010889A CN 116010889 A CN116010889 A CN 116010889A CN 202211725975 A CN202211725975 A CN 202211725975A CN 116010889 A CN116010889 A CN 116010889A
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王凤芹
李瑛�
周立军
刘瑜
张�杰
姜辉
高龙
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Naval Aeronautical University
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Abstract

The invention relates to the technical field of aviation flight data analysis, in particular to an aviation aircraft abnormal state detection method. The method comprises the following steps: 1) Selecting historical standard flight data of the aviation aircraft by aviation aircraft flight experts, and marking the flight phases of the historical standard flight data; 2) Designing an optimization classification regression decision tree CART algorithm by using a marked data set D marked with the flight phases to construct a flight phase classification model M; 3) Inputting flight data of an abnormal flight state to be identified; 4) Performing flight phase division on the flight data set X by using the constructed classification model M; 5) Identifying abnormal flight states in the forest by using an optimized isolated forest algorithm IF; 6) And outputting the abnormal flight state time series data. According to the method, the historical flight data of the aircraft are utilized, a model of a normal flight state is established by a training algorithm, and the established model is utilized to identify an abnormal flight state.

Description

一种航空飞行器异常飞行状态智能识别方法An intelligent identification method for abnormal flight status of aircraft

技术领域Technical Field

本发明涉及航空飞行数据分析技术领域,特别涉及一种航空飞行器异常状态检测方法。The present invention relates to the technical field of aviation flight data analysis, and in particular to a method for detecting abnormal state of an aviation aircraft.

背景技术Background Art

航空飞行器的飞行状态是指航空飞行器在某一瞬间的运动情况。航空飞行器正常飞行状态是指航空飞行器在飞控系统控制下按照预先装订的航线或者期望的状态飞行所处的状态。航空飞行器异常飞行状态是指航空飞行器由于某组成部件发生故障或者受外界环境的干扰、影响,导致飞行状态与期望的飞行状态发生较大偏差。例如,实际飞行航线与设定航线偏差较大,给定姿态角、速度、航向与相应的实测值较长时间内偏差较大,这些航空飞行器异常飞行状态会带来飞行风险,如果不能及时识别与处理,可能会导致重大飞行安全事故。因此,航空飞行器异常飞行状态的识别对保障航空飞行器的飞行安全具有重要意义。航空飞行器飞行的异常状态的识别,为航空飞行器实时操控决策和健康管理提供决策支持,方便航空飞行器保障人员提早查找异常事件原因,避免飞行事故的发生,保证飞行安全。The flight state of an aircraft refers to the movement of an aircraft at a certain moment. The normal flight state of an aircraft refers to the state in which the aircraft flies according to a pre-set route or expected state under the control of the flight control system. The abnormal flight state of an aircraft refers to the flight state of an aircraft that deviates greatly from the expected flight state due to the failure of a component or the interference and influence of the external environment. For example, the actual flight route deviates greatly from the set route, and the given attitude angle, speed, and heading deviate greatly from the corresponding measured values for a long time. These abnormal flight states of aircraft will bring flight risks. If they cannot be identified and handled in time, they may cause major flight safety accidents. Therefore, the identification of abnormal flight states of aircraft is of great significance to ensuring the flight safety of aircraft. The identification of abnormal flight states of aircraft provides decision support for real-time control decisions and health management of aircraft, and facilitates aircraft support personnel to find the causes of abnormal events in advance, avoid flight accidents, and ensure flight safety.

针对飞行状态自动识别问题,谢川等人构建了基于专家知识库和知识推理机的飞行状态识别方法,该识别方法受限于专家知识库。孟光磊等人在文献中以飞行模拟训练的机动动作对应飞参数据为研究对象,构建了机动动作识别的动态贝叶斯网络模型,借助基于网络模型的递归推理的智能方法识别飞行状态。周超等人主要针对战术机动动作数据随机性强和长度不一的特点,提出了基于改进动态时间规整算法的飞行状态识别方法,该方法利用飞行状态的不同特征参数设置不同贡献度,计算飞参数据与标准模板数据的帧匹配距离,根据距离大小进行飞行状态的识别。这些文献都在某具体应用场景下实现了某些飞行状态的自动识别,不适用于航空飞行器异常飞行状态识别的场景。Aiming at the problem of automatic identification of flight status, Xie Chuan et al. constructed a flight status identification method based on expert knowledge base and knowledge inference engine. This identification method is limited by the expert knowledge base. In the literature, Meng Guanglei et al. took the flight parameter data corresponding to the maneuvering action of flight simulation training as the research object, constructed a dynamic Bayesian network model for maneuvering action identification, and identified the flight status with the help of an intelligent method of recursive reasoning based on the network model. Zhou Chao et al. mainly proposed a flight status identification method based on an improved dynamic time warping algorithm, mainly targeting the characteristics of strong randomness and different lengths of tactical maneuvering action data. This method uses different characteristic parameters of the flight status to set different contribution degrees, calculates the frame matching distance between the flight parameter data and the standard template data, and identifies the flight status according to the distance. These documents have realized the automatic identification of certain flight states in a specific application scenario, and are not suitable for the scenario of abnormal flight status identification of aircraft.

因此,亟需出现一种能解决上述技术问题的航空飞行器异常飞行状态智能识别方法。Therefore, there is an urgent need for an intelligent identification method for abnormal flight status of aircraft that can solve the above-mentioned technical problems.

发明内容Summary of the invention

本发明的目的在于克服上述现有技术存在的不足之处,提供一种航空飞行器异常飞行状态智能识别方法,该方法定义了航空飞行器不同飞行阶段的姿态异常、速度异常、航迹异常和执行机构控制异常的多元特征参数,引入网格搜索方法优化分类决策树的参数,实现飞行阶段的准确分类,加入特征参数的异常影响度构建孤立森林,提高异常飞行状态识别的准确率,本发明提出的智能方法在航空飞行器飞行监控方面具有重要应用价值,对提升飞行安全智能化监控能力具有重要意义。The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method for intelligently identifying abnormal flight states of aircraft. The method defines multivariate characteristic parameters of abnormal attitude, abnormal speed, abnormal track and abnormal actuator control in different flight stages of aircraft, introduces a grid search method to optimize the parameters of the classification decision tree, realizes accurate classification of the flight stages, adds the abnormal influence of the characteristic parameters to construct an isolated forest, and improves the accuracy of abnormal flight state identification. The intelligent method proposed in the present invention has important application value in aircraft flight monitoring and is of great significance to improving the intelligent monitoring capability of flight safety.

本发明的一种航空飞行器异常飞行状态智能识别方法,其特殊之处在于包括以下步骤:The invention provides an intelligent identification method for abnormal flight status of an aircraft, which is special in that it comprises the following steps:

1)由航空飞行器飞行专家挑选航空飞行器历史标准飞行数据,对其进行飞行阶段标注;1) Aviation flight experts select historical standard flight data of aviation aircraft and mark the flight phases thereof;

具体为:飞行专家挑选出历史标准飞行数据,对其标注起飞、爬升、平飞、转弯、下降和着陆6个基本飞行阶段,标注后飞行数据集合记为D={D1,D2,......,D6};Specifically, flight experts select historical standard flight data and mark the six basic flight stages of takeoff, climb, level flight, turn, descent and landing. The marked flight data set is recorded as D = {D 1 ,D 2 ,......,D 6 };

2)利用标注好飞行阶段的标注数据集D设计优化分类回归决策树(Classification And Regression Tree,CART)算法构建飞行阶段分类模型M;2) Using the labeled data set D with labeled flight stages, we design and optimize the Classification And Regression Tree (CART) algorithm to build a flight stage classification model M;

具体为:Specifically:

步骤1:对于标准动作模板数据D={D1,D2,......,D6},计算标准飞行状态Dr第u个时间点的n元特征参数向量

Figure BDA0004029946210000021
之间的高度变化率Δh、俯仰角变化率Δα、滚转角变化率Δβ、偏航角变化率Δγ、航向角变化率Δδ,计算数据集D的基尼系数公式(1)Step 1: For the standard action template data D = {D 1 , D 2 , ..., D 6 }, calculate the n-element feature parameter vector of the standard flight state D r at the u-th time point
Figure BDA0004029946210000021
The height change rate Δh, pitch angle change rate Δα, roll angle change rate Δβ, yaw angle change rate Δγ, and heading angle change rate Δδ are used to calculate the Gini coefficient formula of data set D (1):

Figure BDA0004029946210000031
Figure BDA0004029946210000031

式中pk表示数据集D中第k个类别在D中所占比例,比采用信息增益率计算对数的方式简单。对数据集D,计算特征参数c的基尼系数公式(2)In the formula, pk represents the proportion of the kth category in the data set D, which is simpler than the method of calculating the logarithm using the information gain rate. For the data set D, the Gini coefficient formula for calculating the feature parameter c is (2):

Figure BDA0004029946210000032
Figure BDA0004029946210000032

式中|D|表示数据集D所有时间序列向量的数目,E表示特征参数c在数据集D上的取值类别总数,|De|表示数据集合D中所有特征c取值为e的向量总数,Gini(De)是按照式(1)计算基尼系数。Where |D| represents the number of all time series vectors in the data set D, E represents the total number of value categories of the feature parameter c in the data set D, |D e | represents the total number of vectors in the data set D where the feature c value is e, and Gini(D e ) is the Gini coefficient calculated according to formula (1).

步骤2:数据集D依据基尼系数值最小的特征参数C,划分为两部分得到其左右节点,记为Dleft和DrightStep 2: Data set D is divided into two parts according to the characteristic parameter C with the smallest Gini coefficient value to obtain its left and right nodes, denoted as D left and D right ;

步骤3:停止构建决策树的条件有两个,一是判断阈值与当前基尼系数大小关系,若当前基尼系数小于阈值,则当前节点停止构建决策树子树,否则继续构建;二是检查特征参数集合D中是否存在能继续分解集合的分类特征,如果没有找到,则停止构建决策树;Step 3: There are two conditions for stopping the construction of the decision tree. One is to determine the relationship between the threshold and the current Gini coefficient. If the current Gini coefficient is less than the threshold, the current node stops building the decision tree subtree, otherwise it continues to build. The second is to check whether there is a classification feature in the feature parameter set D that can continue to decompose the set. If not found, stop building the decision tree.

步骤4:对D节点的左右子节点Dleft和Dright递归执行步骤1至步骤4,直到从满足步骤3的条件退出,返回决策树训练模型M;Step 4: Recursively execute steps 1 to 4 for the left and right child nodes D left and D right of the D node until the condition of step 3 is met and the decision tree training model M is returned.

步骤5:以避免决策树模型的过拟合问题,对决策树进行剪枝。所谓剪枝,删除非叶子节点{T1,T2,T3,......,Tn}中表面误差率增益值最小的αi对应非叶子节点Ti的左右子节点,αi的计算公式(3)为Step 5: To avoid the overfitting problem of the decision tree model, prune the decision tree. Pruning means deleting the left and right child nodes of the non-leaf node T i corresponding to the α i with the smallest surface error rate gain value among the non-leaf nodes {T 1 ,T 2 ,T 3 ,......,T n }. The calculation formula (3) of α i is:

Figure BDA0004029946210000033
Figure BDA0004029946210000033

式中R(i)表示叶子节点替换第i个非叶子节点后产生的误差,计算公式为Where R(i) represents the error generated after the leaf node replaces the i-th non-leaf node, and the calculation formula is:

R(i)=r(i)p(i), (4)R(i)=r(i)p(i), (4)

式中r(i)表示节点i的误差率,p(i)表示节点i上的样本个数占整个训练集中样本个数的百分比,R(Ti)表示节点i没有裁剪时子树Ti上所有叶子节点的误差之和,即Where r(i) represents the error rate of node i, p(i) represents the percentage of samples on node i to the total number of samples in the training set, and R(T i ) represents the sum of the errors of all leaf nodes on subtree T i when node i is not pruned, that is,

Figure BDA0004029946210000041
Figure BDA0004029946210000041

用子树的叶子节点替换非叶子节点Ti,重复该过程直至没有任何非叶子节点可以替换,剪枝完成,返回决策树Tree;Replace the non-leaf node Ti with the leaf node of the subtree, repeat the process until there is no non-leaf node to replace, pruning is completed, and return to the decision tree Tree;

步骤6:设定目标损失函数(准确率、精确率、召回率或F1分数),将决策树所有超参放置于网络中,利用网格搜索算法对决策树参数进行调整,使得决策树模型M分类最优,同时,为了避免训练集和测试集划分带来的影响,采用数据交叉验证的方式,提高决策树模型M的分类性能;Step 6: Set the target loss function (accuracy, precision, recall or F1 score), place all the hyperparameters of the decision tree in the network, and use the grid search algorithm to adjust the decision tree parameters to make the decision tree model M optimally classified. At the same time, in order to avoid the impact of the division of training sets and test sets, use data cross-validation to improve the classification performance of the decision tree model M.

3)输入待识别异常飞行状态的飞行数据;3) Input flight data of abnormal flight status to be identified;

具体为:输入t个时间点的航空飞行器飞行数据集合记为X={X1,X2,……,Xt}Specifically, the flight data set of an aircraft at t time points is input and recorded as X = {X 1 , X 2 , ..., X t }

4)利用构建好的分类模型M对飞行数据集合X进行飞行阶段划分;4) Use the constructed classification model M to divide the flight data set X into flight stages;

具体为:利用分类模型M对t个时间点的飞行数据集合X={X1,X2,…,Xt}进行飞行阶段的自动化划分,标注各时间段的飞行阶段;Specifically, the classification model M is used to automatically divide the flight data set X = {X 1 , X 2 , …, X t } at t time points into flight phases, and the flight phases of each time period are marked;

5)利用优化孤立森林算法(Isolation Forest,IF)识别其中的异常飞行状态;5) Using the optimized Isolation Forest (IF) algorithm to identify abnormal flight status;

具体为:Specifically:

步骤1:对已标注好飞行阶段的X,依据表1计算各飞行阶段的异常特征参数值,各飞行阶段的特征参数集合记为S;Step 1: For X with marked flight stages, calculate the abnormal characteristic parameter values of each flight stage according to Table 1, and the characteristic parameter set of each flight stage is recorded as S;

起飞、爬升、平飞、转弯、下降和着陆共6个最基本飞行阶段为例,每个飞行阶段可以用飞行速度、高度、姿态角和航向角等特征参数进行表征,同时每个飞行阶段都对应不同的飞行控制律,建立飞行阶段的主要特征参数;Taking the six most basic flight stages of takeoff, climb, level flight, turn, descent and landing as an example, each flight stage can be characterized by characteristic parameters such as flight speed, altitude, attitude angle and heading angle. At the same time, each flight stage corresponds to a different flight control law, and the main characteristic parameters of the flight stage are established;

其中,异常特征参数归类为:姿态异常、速度异常、航迹异常和执行机构控制异常,各类参数的计算方法如下:Among them, the abnormal characteristic parameters are classified into: abnormal attitude, abnormal speed, abnormal track and abnormal actuator control. The calculation methods of various parameters are as follows:

(1)姿态异常特征参数(1) Abnormal posture characteristic parameters

航空飞行器飞行姿态异常是指姿态角(俯仰角θ、偏航角ψ、滚转角φ)的测量值较长时间偏离给定值(给定俯仰角θ_ref、给定偏航角ψ_ref、给定滚转角φ_ref),计算个N时刻点各姿态角的偏差均值(E(Δθ)、E(Δψ)和E(Δφ))作为姿态异常特征参数,即The abnormal flight attitude of an aircraft refers to the deviation of the measured values of the attitude angles (pitch angle θ, yaw angle ψ, roll angle φ) from the given values (given pitch angle θ_ref, given yaw angle ψ_ref, given roll angle φ_ref) for a long time. The mean deviation of each attitude angle at N time points (E(Δθ), E(Δψ) and E(Δφ)) is calculated as the characteristic parameter of the attitude abnormality, that is,

Figure BDA0004029946210000051
Figure BDA0004029946210000051

其中,θi、ψi、φi分别表示第i(i=1,2,...,N)个时刻点俯仰角、偏航角和滚转角的测量值,θ_refi、ψ_refi、φ_refi分别表示第i(i=1,2,...,N)个时刻点俯仰角、偏航角和滚转角的给定值。Among them, θ i , ψ i , φ i respectively represent the measured values of the pitch angle, yaw angle and roll angle at the i-th (i=1, 2, ..., N) time point, and θ_ref i , ψ_ref i , φ_ref i respectively represent the given values of the pitch angle, yaw angle and roll angle at the i-th (i=1, 2, ..., N) time point.

(2)速度异常特征参数(2) Speed anomaly characteristic parameters

航空飞行器速度异常是指飞行速度的测量值V较长时间偏离给定速度值V_ref,计算个N时刻点速度的偏差均值E(ΔV)作为速度异常特征参数,即The speed anomaly of an aircraft refers to the fact that the measured value V of the flight speed deviates from the given speed value V_ref for a long time. The mean deviation of the speed at N time points E(ΔV) is calculated as the speed anomaly characteristic parameter, that is,

Figure BDA0004029946210000052
Figure BDA0004029946210000052

其中,Vi、V_refi分别表示第i(i=1,2,...,N)个时刻点航空飞行器飞行速度测量值和给定值。Wherein, V i and V_ref i represent the measured value and given value of the flight speed of the aircraft at the i-th (i=1, 2, ..., N) time point, respectively.

(3)航迹异常特征参数(3) Track anomaly characteristic parameters

航空飞行器航迹异常是指实际飞行航线偏离设定航线,航线是由若干航点构成,计算N个时刻点航点的距离偏差均值E(Δd)作为航迹异常特征参数,即The abnormal flight path of an aircraft refers to the deviation of the actual flight route from the set route. The route is composed of several waypoints. The mean distance deviation E(Δd) of the waypoints at N time points is calculated as the characteristic parameter of the abnormal flight path, that is,

Figure BDA0004029946210000061
Figure BDA0004029946210000061

其中,第i(i=1,2,...,N)时刻的航点位置经度、纬度和高度的测量值和给定值分别表示为Pi(xi,yi,hi)和P_refi(x_refi,y_refi,h_refi)。Among them, the measured values and given values of the longitude, latitude and altitude of the waypoint position at the i-th (i=1, 2, ..., N) moment are represented as Pi ( xi , yi , hi ) and P_refi ( x_refi , y_refi , h_refi ), respectively.

计算N个时刻点航点的航向角偏差均值E(ΔPSI)作为航迹异常特征参数,即Calculate the mean value of the heading angle deviation E(ΔPSI) of the waypoints at N time points as the track anomaly characteristic parameter, that is,

Figure BDA0004029946210000062
Figure BDA0004029946210000062

其中,PSIi、PSI_refi分别表示第i(i=1,2,...,N)时刻航向角的测量值和给定值。Wherein, PSI i and PSI_ref i represent the measured value and given value of the heading angle at the i-th (i=1, 2, ..., N) moment respectively.

(4)执行机构控制异常(4) Abnormal actuator control

航空飞行器执行机构控制异常是指执行机构位移(副翼位移dtx、方向舵位移dty、升降舵位移dtz、襟副翼位移dtjy、前轮位移dtw)偏离执行机构指令(副翼指令dtxc、方向舵指令dtyc、升降舵指令dtzc、襟副翼指令dtjyc、前轮指令dtwc),计算个N时刻点各执行机构指令的偏差均值(E(Δdtx)、E(Δdty)、E(Δdtz)、E(Δdtjy)和E(Δdtw))作为执行机构控制异常特征参数,即The abnormal control of the actuator of an aircraft refers to the deviation of the actuator displacement (aileron displacement dtx, rudder displacement dty, elevator displacement dtz, flaperon displacement dtjy, and front wheel displacement dtw) from the actuator command (aileron command dtxc, rudder command dtyc, elevator command dtzc, flaperon command dtjyc, and front wheel command dtwc). The deviation mean of the actuator commands at N time points (E(Δdtx), E(Δdty), E(Δdtz), E(Δdtjy), and E(Δdtw)) is calculated as the characteristic parameter of the actuator control abnormality, that is,

Figure BDA0004029946210000071
Figure BDA0004029946210000071

其中,dtxi、dtyi、dtzi、dtjyi、dtwi分别表示第i(i=1,2,...,N)个时刻点副翼位移、方向舵位移、升降舵位移、襟副翼位移、前轮位移的测量值,dtxci、dtyci、dtzci、dtjyci、dtwci分别表示第i(i=1,2,...,N)个时刻点副翼指令、方向舵指令、升降舵指令、襟副翼指令和前轮指令的给定值;Wherein, dtx i , dty i , dtz i , dtjy i , dtw i represent the measured values of aileron displacement, rudder displacement, elevator displacement, flaperon displacement, and nosewheel displacement at the i-th (i=1, 2, ..., N) time point, respectively; dtxc i , dtyc i , dtzc i , dtjyc i , dtwc i represent the given values of aileron command, rudder command, elevator command, flaperon command, and nosewheel command at the i-th (i=1, 2, ..., N) time point, respectively;

步骤2:从异常特征参数集合S中选取异常影响最大的特征参数及选取最接近均值的数据点作为树的根节点。Step 2: Select the feature parameter with the greatest abnormal impact from the abnormal feature parameter set S and select the data point closest to the mean as the root node of the tree.

步骤3:将异常特征参数值小于当前分割点的设置为二叉树的左节点Left,大于等于的设置为二叉树的右节点Right。Step 3: Set the abnormal feature parameter value that is less than the current split point as the left node Left of the binary tree, and set the abnormal feature parameter value that is greater than or equal to the current split point as the right node Right of the binary tree.

步骤4:节点Left和Right重复步骤9和步骤10递归构造树,直到只有一个数据无法继续构造,或者树已达到限定高度,停止构造。Step 4: Repeat steps 9 and 10 for nodes Left and Right to recursively construct the tree until only one data cannot be constructed further or the tree has reached a limited height, then stop constructing.

步骤5:计算样本x的异常分数s(x,n):Step 5: Calculate the anomaly score s(x,n) of sample x:

Figure BDA0004029946210000072
Figure BDA0004029946210000072

式中:h(x)样本x所在树的路径长度,c(n)为每棵树的路径平均长度[197]Where h(x) is the path length of the tree where sample x is located, and c(n) is the average path length of each tree [197] :

Figure BDA0004029946210000073
Figure BDA0004029946210000073

式中:H(n-1)=ln(n-1)+ζ(ζ为欧拉常数,一般取值0.58)。对于异常分数s(x,n)接近1的数据样本x为异常数据点。Where: H(n-1) = ln(n-1) + ζ (ζ is the Euler constant, generally 0.58). For data samples x whose abnormal score s(x,n) is close to 1, they are abnormal data points.

6)输出异常飞行状态时间序列数据。6) Output abnormal flight status time series data.

具体为:输出航空飞行器飞行数据集合X={X1,X2,……,Xt}中异常飞行状态时间序列。Specifically, the abnormal flight state time series in the aviation aircraft flight data set X = {X 1 , X 2 , ..., X t } is output.

本发明的一种航空飞行器异常飞行状态智能识别方法,能对航空飞行器飞行异常状态的识别,为航空飞行器实时操控决策和健康管理提供决策支持,方便航空飞行器保障人员查找异常事件原因,避免飞行事故的发生,保证飞行安全。The intelligent identification method of abnormal flight status of an aircraft of the present invention can identify the abnormal flight status of an aircraft, provide decision support for real-time control decision-making and health management of the aircraft, facilitate aircraft support personnel to find the cause of abnormal events, avoid the occurrence of flight accidents, and ensure flight safety.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

图1为一种航空飞行器异常飞行状态智能识别方法的流程图。FIG1 is a flow chart of a method for intelligently identifying abnormal flight status of an aircraft.

具体实施方式DETAILED DESCRIPTION

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。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.

实施例1Example 1

本实施例的一种航空飞行器异常飞行状态智能识别方法,请参阅图1,包括以下步骤:A method for intelligently identifying abnormal flight status of an aircraft in this embodiment, as shown in FIG1 , includes the following steps:

1)由航空飞行器飞行专家挑选航空飞行器历史标准飞行数据,对其进行飞行阶段标注;1) Aviation flight experts select historical standard flight data of aviation aircraft and mark the flight phases thereof;

具体为:飞行专家挑选出历史标准飞行数据,对其标注起飞、爬升、平飞、转弯、下降和着陆6个基本飞行阶段,标注后飞行数据集合记为D={D1,D2,......,D6};Specifically, flight experts select historical standard flight data and mark the six basic flight stages of takeoff, climb, level flight, turn, descent and landing. The marked flight data set is recorded as D = {D 1 ,D 2 ,......,D 6 };

2)利用标注好飞行阶段的标注数据集D设计优化分类回归决策树CART算法构建飞行阶段分类模型M;2) Using the labeled data set D with labeled flight stages, we design and optimize the classification regression decision tree CART algorithm to build a flight stage classification model M;

具体为:Specifically:

步骤1:对于标准动作模板数据D={D1,D2,......,D6},计算标准飞行状态Dr第u个时间点的n元特征参数向量

Figure BDA0004029946210000091
之间的高度变化率Δh、俯仰角变化率Δα、滚转角变化率Δβ、偏航角变化率Δγ、航向角变化率Δδ,计算数据集D的基尼系数公式(1)Step 1: For the standard action template data D = {D 1 , D 2 , ..., D 6 }, calculate the n-element feature parameter vector of the standard flight state D r at the u-th time point
Figure BDA0004029946210000091
The height change rate Δh, pitch angle change rate Δα, roll angle change rate Δβ, yaw angle change rate Δγ, and heading angle change rate Δδ are used to calculate the Gini coefficient formula of data set D (1):

Figure BDA0004029946210000092
Figure BDA0004029946210000092

式中pk表示数据集D中第k个类别在D中所占比例,比采用信息增益率计算对数的方式简单。对数据集D,计算特征参数c的基尼系数公式(2)In the formula, pk represents the proportion of the kth category in the data set D, which is simpler than the method of calculating the logarithm using the information gain rate. For the data set D, the Gini coefficient formula for calculating the feature parameter c is (2):

Figure BDA0004029946210000093
Figure BDA0004029946210000093

式中|D|表示数据集D所有时间序列向量的数目,E表示特征参数c在数据集D上的取值类别总数,|De|表示数据集合D中所有特征c取值为e的向量总数,Gini(De)是按照式(1)计算基尼系数。Where |D| represents the number of all time series vectors in the data set D, E represents the total number of value categories of the feature parameter c in the data set D, |D e | represents the total number of vectors in the data set D where the feature c value is e, and Gini(D e ) is the Gini coefficient calculated according to formula (1).

步骤2:数据集D依据基尼系数值最小的特征参数C,划分为两部分得到其左右节点,记为Dleft和DrightStep 2: Data set D is divided into two parts according to the characteristic parameter C with the smallest Gini coefficient value to obtain its left and right nodes, denoted as D left and D right ;

步骤3:停止构建决策树的条件有两个,一是判断阈值与当前基尼系数大小关系,若当前基尼系数小于阈值,则当前节点停止构建决策树子树,否则继续构建;二是检查特征参数集合D中是否存在能继续分解集合的分类特征,如果没有找到,则停止构建决策树;Step 3: There are two conditions for stopping the construction of the decision tree. One is to determine the relationship between the threshold and the current Gini coefficient. If the current Gini coefficient is less than the threshold, the current node stops building the decision tree subtree, otherwise it continues to build. The second is to check whether there is a classification feature in the feature parameter set D that can continue to decompose the set. If not found, stop building the decision tree.

步骤4:对D节点的左右子节点Dleft和Dright递归执行步骤1至步骤4,直到从满足步骤3的条件退出,返回决策树训练模型Tree;Step 4: Recursively execute steps 1 to 4 for the left and right child nodes D left and D right of the D node until the condition of step 3 is met and the decision tree training model Tree is returned;

步骤5:以避免决策树模型的过拟合问题,对决策树进行剪枝。所谓剪枝,删除非叶子节点{T1,T2,T3,......,Tn}中表面误差率增益值最小的αi对应非叶子节点Ti的左右子节点,αi的计算公式(3)为Step 5: To avoid the overfitting problem of the decision tree model, prune the decision tree. Pruning means deleting the left and right child nodes of the non-leaf node T i corresponding to the α i with the smallest surface error rate gain value among the non-leaf nodes {T 1 ,T 2 ,T 3 ,......,T n }. The calculation formula (3) of α i is:

Figure BDA0004029946210000101
Figure BDA0004029946210000101

式中R(i)表示叶子节点替换第i个非叶子节点后产生的误差,计算公式为Where R(i) represents the error generated after the leaf node replaces the i-th non-leaf node, and the calculation formula is:

R(i)=r(i)p(i), (4)R(i)=r(i)p(i), (4)

式中r(i)表示节点i的误差率,p(i)表示节点i上的样本个数占整个训练集中样本个数的百分比,R(Ti)表示节点i没有裁剪时子树Ti上所有叶子节点的误差之和,即Where r(i) represents the error rate of node i, p(i) represents the percentage of samples on node i to the total number of samples in the training set, and R(T i ) represents the sum of the errors of all leaf nodes on subtree T i when node i is not pruned, that is,

Figure BDA0004029946210000102
Figure BDA0004029946210000102

用子树的叶子节点替换非叶子节点Ti,重复该过程直至没有任何非叶子节点可以替换,剪枝完成,返回决策树Tree;Replace the non-leaf node Ti with the leaf node of the subtree, repeat the process until there is no non-leaf node to replace, pruning is completed, and return to the decision tree Tree;

步骤6:设定目标损失函数(准确率、精确率、召回率或F1分数),将决策树所有超参放置于网络中,利用网格搜索算法对决策树参数进行调整,使得决策树模型M分类最优,同时,为了避免训练集和测试集划分带来的影响,采用数据交叉验证的方式,提高决策树模型M的分类性能;Step 6: Set the target loss function (accuracy, precision, recall or F1 score), place all the hyperparameters of the decision tree in the network, and use the grid search algorithm to adjust the decision tree parameters to make the decision tree model M optimally classified. At the same time, in order to avoid the impact of the division of the training set and the test set, use data cross-validation to improve the classification performance of the decision tree model M;

3)输入待识别异常飞行状态的飞行数据;3) Input flight data of abnormal flight status to be identified;

具体为:输入t个时间点的航空飞行器飞行数据集合记为X={X1,X2,……,Xt}Specifically, the flight data set of an aircraft at t time points is input and recorded as X = {X 1 , X 2 , ..., X t }

4)利用构建好的分类模型M对飞行数据集合X进行飞行阶段划分;4) Use the constructed classification model M to divide the flight data set X into flight stages;

具体为:利用分类模型M对t个时间点的飞行数据集合X={X1,X2,…,Xt}进行飞行阶段的自动化划分,标注各时间段的飞行阶段;Specifically, the classification model M is used to automatically divide the flight data set X = {X 1 , X 2 , …, X t } at t time points into flight phases, and the flight phases of each time period are marked;

5)利用优化孤立森林算法IF识别其中的异常飞行状态;5) Use the optimized isolation forest algorithm IF to identify abnormal flight status;

具体为:Specifically:

步骤1:对已标注好飞行阶段的X,依据表1计算各飞行阶段的异常特征参数值,各飞行阶段的特征参数集合记为S;Step 1: For X with marked flight stages, calculate the abnormal characteristic parameter values of each flight stage according to Table 1, and the characteristic parameter set of each flight stage is recorded as S;

起飞、爬升、平飞、转弯、下降和着陆共6个最基本飞行阶段为例,每个飞行阶段可以用飞行速度、高度、姿态角和航向角等特征参数进行表征,同时每个飞行阶段都对应不同的飞行控制律,建立飞行阶段的主要特征参数,如表1所示。Taking the six most basic flight stages of takeoff, climb, level flight, turn, descent and landing as examples, each flight stage can be characterized by characteristic parameters such as flight speed, altitude, attitude angle and heading angle. At the same time, each flight stage corresponds to a different flight control law, and the main characteristic parameters of the flight stage are established, as shown in Table 1.

表1航空飞行器飞行阶段特征参数表Table 1 Characteristic parameters of aircraft flight phases

Figure BDA0004029946210000111
Figure BDA0004029946210000111

Figure BDA0004029946210000121
Figure BDA0004029946210000121

表中异常特征参数归类为:姿态异常、速度异常、航迹异常和执行机构控制异常,各类参数的计算方法如下:The abnormal characteristic parameters in the table are classified into: abnormal attitude, abnormal speed, abnormal track and abnormal actuator control. The calculation methods of various parameters are as follows:

(1)姿态异常特征参数(1) Abnormal posture characteristic parameters

航空飞行器飞行姿态异常是指姿态角(俯仰角θ、偏航角ψ、滚转角φ)的测量值较长时间偏离给定值(给定俯仰角θ_ref、给定偏航角ψ_ref、给定滚转角φ_ref),计算个N时刻点各姿态角的偏差均值(E(Δθ)、E(Δψ)和E(Δφ))作为姿态异常特征参数,即The abnormal flight attitude of an aircraft refers to the deviation of the measured values of the attitude angles (pitch angle θ, yaw angle ψ, roll angle φ) from the given values (given pitch angle θ_ref, given yaw angle ψ_ref, given roll angle φ_ref) for a long time. The mean deviation of each attitude angle at N time points (E(Δθ), E(Δψ) and E(Δφ)) is calculated as the characteristic parameter of the attitude abnormality, that is,

Figure BDA0004029946210000122
Figure BDA0004029946210000122

其中,θi、ψi、φi分别表示第i(i=1,2,...,N)个时刻点俯仰角、偏航角和滚转角的测量值,θ_refi、ψ_refi、φ_refi分别表示第i(i=1,2,...,N)个时刻点俯仰角、偏航角和滚转角的给定值。Among them, θ i , ψ i , φ i respectively represent the measured values of the pitch angle, yaw angle and roll angle at the i-th (i=1, 2, ..., N) time point, and θ_ref i , ψ_ref i , φ_ref i respectively represent the given values of the pitch angle, yaw angle and roll angle at the i-th (i=1, 2, ..., N) time point.

(2)速度异常特征参数(2) Speed anomaly characteristic parameters

航空飞行器速度异常是指飞行速度的测量值V较长时间偏离给定速度值V_ref,计算个N时刻点速度的偏差均值E(ΔV)作为速度异常特征参数,即The speed anomaly of an aircraft refers to the fact that the measured value V of the flight speed deviates from the given speed value V_ref for a long time. The mean deviation of the speed at N time points E(ΔV) is calculated as the speed anomaly characteristic parameter, that is,

Figure BDA0004029946210000123
Figure BDA0004029946210000123

其中,Vi、V_refi分别表示第i(i=1,2,...,N)个时刻点航空飞行器飞行速度测量值和给定值。Wherein, V i and V_ref i represent the measured value and given value of the flight speed of the aircraft at the i-th (i=1, 2, ..., N) time point, respectively.

(3)航迹异常特征参数(3) Track anomaly characteristic parameters

航空飞行器航迹异常是指实际飞行航线偏离设定航线,航线是由若干航点构成,计算N个时刻点航点的距离偏差均值E(Δd)作为航迹异常特征参数,即The abnormal flight path of an aircraft refers to the deviation of the actual flight route from the set route. The route is composed of several waypoints. The mean distance deviation E(Δd) of the waypoints at N time points is calculated as the characteristic parameter of the abnormal flight path, that is,

Figure BDA0004029946210000131
Figure BDA0004029946210000131

其中,第i(i=1,2,...,N)时刻的航点位置经度、纬度和高度的测量值和给定值分别表示为Pi(xi,yi,hi)和P_refi(x_refi,y_refi,h_refi)。Among them, the measured values and given values of the longitude, latitude and altitude of the waypoint position at the i-th (i=1, 2, ..., N) moment are represented as Pi ( xi , yi , hi ) and P_refi ( x_refi , y_refi , h_refi ), respectively.

计算N个时刻点航点的航向角偏差均值E(ΔPSI)作为航迹异常特征参数,即Calculate the mean value of the heading angle deviation E(ΔPSI) of the waypoints at N time points as the track anomaly characteristic parameter, that is,

Figure BDA0004029946210000132
Figure BDA0004029946210000132

其中,PSIi、PSI_refi分别表示第i(i=1,2,...,N)时刻航向角的测量值和给定值。Wherein, PSI i and PSI_ref i represent the measured value and given value of the heading angle at the i-th (i=1, 2, ..., N) moment respectively.

(4)执行机构控制异常(4) Abnormal actuator control

航空飞行器执行机构控制异常是指执行机构位移(副翼位移dtx、方向舵位移dty、升降舵位移dtz、襟副翼位移dtjy、前轮位移dtw)偏离执行机构指令(副翼指令dtxc、方向舵指令dtyc、升降舵指令dtzc、襟副翼指令dtjyc、前轮指令dtwc),计算个N时刻点各执行机构指令的偏差均值(E(Δdtx)、E(Δdty)、E(Δdtz)、E(Δdtjy)和E(Δdtw))作为执行机构控制异常特征参数,即The abnormal control of the actuator of an aircraft refers to the deviation of the actuator displacement (aileron displacement dtx, rudder displacement dty, elevator displacement dtz, flaperon displacement dtjy, and front wheel displacement dtw) from the actuator command (aileron command dtxc, rudder command dtyc, elevator command dtzc, flaperon command dtjyc, and front wheel command dtwc). The deviation mean of the actuator commands at N time points (E(Δdtx), E(Δdty), E(Δdtz), E(Δdtjy), and E(Δdtw)) is calculated as the characteristic parameter of the actuator control abnormality, that is,

Figure BDA0004029946210000141
Figure BDA0004029946210000141

其中,dtxi、dtyi、dtzi、dtjyi、dtwi分别表示第i(i=1,2,...,N)个时刻点副翼位移、方向舵位移、升降舵位移、襟副翼位移、前轮位移的测量值,dtxci、dtyci、dtzci、dtjyci、dtwci分别表示第i(i=1,2,...,N)个时刻点副翼指令、方向舵指令、升降舵指令、襟副翼指令和前轮指令的给定值;Wherein, dtx i , dty i , dtz i , dtjy i , dtw i represent the measured values of aileron displacement, rudder displacement, elevator displacement, flaperon displacement, and nosewheel displacement at the i-th (i=1, 2, ..., N) time point, respectively; dtxc i , dtyc i , dtzc i , dtjyc i , dtwc i represent the given values of aileron command, rudder command, elevator command, flaperon command, and nosewheel command at the i-th (i=1, 2, ..., N) time point, respectively;

步骤2:从异常特征参数集合S中选取异常影响最大的特征参数及选取最接近均值的数据点作为树的根节点。Step 2: Select the feature parameter with the greatest abnormal impact from the abnormal feature parameter set S and select the data point closest to the mean as the root node of the tree.

步骤3:将异常特征参数值小于当前分割点的设置为二叉树的左节点Left,大于等于的设置为二叉树的右节点Right。Step 3: Set the abnormal feature parameter value that is less than the current split point as the left node Left of the binary tree, and set the abnormal feature parameter value that is greater than or equal to the current split point as the right node Right of the binary tree.

步骤4:节点Left和Right重复步骤2和步骤3递归构造树,直到只有一个数据无法继续构造,或者树已达到限定高度,停止构造。Step 4: Nodes Left and Right repeat steps 2 and 3 to recursively construct the tree until only one data cannot be constructed further, or the tree has reached a limited height, and then stop constructing.

步骤5:计算样本x的异常分数s(x,n):Step 5: Calculate the anomaly score s(x,n) of sample x:

Figure BDA0004029946210000142
Figure BDA0004029946210000142

式中:h(x)样本x所在树的路径长度,c(n)为每棵树的路径平均长度:Where: h(x) is the path length of the tree where sample x is located, and c(n) is the average path length of each tree:

Figure BDA0004029946210000143
Figure BDA0004029946210000143

式中:H(n-1)=ln(n-1)+ζ(ζ为欧拉常数,一般取值0.58)。对于异常分数s(x,n)接近1的数据样本x为异常数据点。Where: H(n-1) = ln(n-1) + ζ (ζ is the Euler constant, generally 0.58). For data samples x whose abnormal score s(x,n) is close to 1, they are abnormal data points.

6)输出异常飞行状态时间序列数据。6) Output abnormal flight status time series data.

具体为:输出航空飞行器飞行数据集合X={X1,X2,……,Xt}中异常飞行状态时间序列。Specifically, the abnormal flight state time series in the aviation aircraft flight data set X = {X 1 , X 2 , ..., X t } is output.

尽管已经示出和描述了本发明的实施例,对于本领域的普通技术人员而言,可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型,本发明的范围由所附权利要求及其等同物限定。Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims (3)

1.一种航空飞行器异常飞行状态智能识别方法,其特征在于包括以下步骤:1. A method for intelligently identifying abnormal flight status of an aircraft, characterized by comprising the following steps: 1)由航空飞行器飞行专家挑选航空飞行器历史标准飞行数据,对其进行飞行阶段标注;1) Aviation flight experts select historical standard flight data of aviation aircraft and mark the flight phases thereof; 具体为:飞行专家挑选出历史标准飞行数据,对其标注起飞、爬升、平飞、转弯、下降和着陆6个基本飞行阶段,标注后飞行数据集合记为D={D1,D2,......,D6};Specifically, flight experts select historical standard flight data and mark the six basic flight stages of takeoff, climb, level flight, turn, descent and landing. The marked flight data set is recorded as D = {D 1 ,D 2 ,......,D 6 }; 2)利用标注好飞行阶段的标注数据集D设计优化分类回归决策树算法构建飞行阶段分类模型M;2) Using the labeled data set D with labeled flight stages, we design and optimize the classification regression decision tree algorithm to construct the flight stage classification model M; 3)输入待识别异常飞行状态的飞行数据;3) Input flight data of abnormal flight status to be identified; 具体为:输入t个时间点的航空飞行器飞行数据集合记为X={X1,X2,……,Xt};Specifically, the flight data set of an aircraft at t time points is input and recorded as X = {X 1 , X 2 , ..., X t }; 4)利用构建好的分类模型M对飞行数据集合X进行飞行阶段划分;4) Use the constructed classification model M to divide the flight data set X into flight stages; 具体为:利用分类模型M对t个时间点的飞行数据集合X={X1,X2,…,Xt}进行飞行阶段的自动化划分,标注各时间段的飞行阶段;Specifically, the classification model M is used to automatically divide the flight data set X = {X 1 , X 2 , …, X t } at t time points into flight phases, and the flight phases of each time period are marked; 5)利用优化孤立森林算法识别其中的异常飞行状态;5) Use the optimized isolation forest algorithm to identify abnormal flight status; 6)输出异常飞行状态时间序列数据;6) Output abnormal flight status time series data; 具体为:输出航空飞行器飞行数据集合X={X1,X2,……,Xt}中异常飞行状态时间序列。Specifically, the abnormal flight state time series in the aviation aircraft flight data set X = {X 1 , X 2 , ..., X t } is output. 2.按照权利要求1所述的一种航空飞行器异常飞行状态智能识别方法,其特征在于步骤2)具体包括以下步骤:2. According to the method for intelligently identifying abnormal flight status of an aircraft according to claim 1, it is characterized in that step 2) specifically comprises the following steps: 步骤1:对于标准动作模板数据D={D1,D2,......,D6},计算标准飞行状态Dr第u个时间点的n元特征参数向量
Figure FDA0004029946200000011
之间的高度变化率Δh、俯仰角变化率Δα、滚转角变化率Δβ、偏航角变化率Δγ、航向角变化率Δδ,计算数据集D的基尼系数公式(1)
Step 1: For the standard action template data D = {D 1 , D 2 , ..., D 6 }, calculate the n-element feature parameter vector of the standard flight state D r at the u-th time point
Figure FDA0004029946200000011
The height change rate Δh, pitch angle change rate Δα, roll angle change rate Δβ, yaw angle change rate Δγ, and heading angle change rate Δδ are used to calculate the Gini coefficient formula of data set D (1):
Figure FDA0004029946200000021
Figure FDA0004029946200000021
式中pk表示数据集D中第k个类别在D中所占比例,比采用信息增益率计算对数的方式简单。对数据集D,计算特征参数c的基尼系数公式(2)In the formula, pk represents the proportion of the kth category in the data set D, which is simpler than the method of calculating the logarithm using the information gain rate. For the data set D, the Gini coefficient formula for calculating the feature parameter c is (2):
Figure FDA0004029946200000022
Figure FDA0004029946200000022
式中|D|表示数据集D所有时间序列向量的数目,E表示特征参数c在数据集D上的取值类别总数,|De|表示数据集合D中所有特征c取值为e的向量总数,Gini(De)是按照式(1)计算基尼系数;Where |D| represents the number of all time series vectors in the data set D, E represents the total number of value categories of the feature parameter c in the data set D, |D e | represents the total number of vectors in the data set D where the feature c value is e, and Gini(D e ) is the Gini coefficient calculated according to formula (1); 步骤2:数据集D依据基尼系数值最小的特征参数C,划分为两部分得到其左右节点,记为Dleft和DrightStep 2: Data set D is divided into two parts according to the characteristic parameter C with the smallest Gini coefficient value to obtain its left and right nodes, denoted as D left and D right ; 步骤3:停止构建决策树的条件有两个,一是判断阈值与当前基尼系数大小关系,若当前基尼系数小于阈值,则当前节点停止构建决策树子树,否则继续构建;二是检查特征参数集合D中是否存在能继续分解集合的分类特征,如果没有找到,则停止构建决策树;Step 3: There are two conditions for stopping the construction of the decision tree. One is to determine the relationship between the threshold and the current Gini coefficient. If the current Gini coefficient is less than the threshold, the current node stops building the decision tree subtree, otherwise it continues to build. The second is to check whether there is a classification feature in the feature parameter set D that can continue to decompose the set. If not found, stop building the decision tree. 步骤4:对D节点的左右子节点Dleft和Dright递归执行步骤1至步骤4,直到从满足步骤3的条件退出,返回决策树训练模型M;Step 4: Recursively execute steps 1 to 4 for the left and right child nodes D left and D right of the D node until the condition of step 3 is met and the decision tree training model M is returned. 步骤5:以避免决策树模型的过拟合问题,对决策树进行剪枝,所谓剪枝,删除非叶子节点{T1,T2,T3,......,Tn}中表面误差率增益值最小的αi对应非叶子节点Ti的左右子节点,αi的计算公式(3)为Step 5: To avoid the overfitting problem of the decision tree model, the decision tree is pruned. The so-called pruning is to delete the left and right child nodes of the non-leaf node T i corresponding to the α i with the smallest surface error rate gain value among the non-leaf nodes {T 1 ,T 2 ,T 3 ,......,T n }. The calculation formula (3) of α i is:
Figure FDA0004029946200000031
Figure FDA0004029946200000031
式中R(i)表示叶子节点替换第i个非叶子节点后产生的误差,计算公式为Where R(i) represents the error generated after the leaf node replaces the i-th non-leaf node, and the calculation formula is: R(i)=r(i)p(i), (4)R(i)=r(i)p(i), (4) 式中r(i)表示节点i的误差率,p(i)表示节点i上的样本个数占整个训练集中样本个数的百分比,R(Ti)表示节点i没有裁剪时子树Ti上所有叶子节点的误差之和,即Where r(i) represents the error rate of node i, p(i) represents the percentage of samples on node i to the total number of samples in the training set, and R(T i ) represents the sum of the errors of all leaf nodes on subtree T i when node i is not pruned, that is,
Figure FDA0004029946200000032
Figure FDA0004029946200000032
用子树的叶子节点替换非叶子节点Ti,重复该过程直至没有任何非叶子节点可以替换,剪枝完成,返回决策树M;Replace the non-leaf node Ti with the leaf node of the subtree, repeat the process until there is no non-leaf node to replace, pruning is completed, and return to the decision tree M; 步骤6:设定目标损失函数,将决策树所有超参放置于网络中,利用网格搜索算法对决策树参数进行调整,使得决策树模型M分类最优。同时,为了避免训练集和测试集划分带来的影响,采用数据交叉验证的方式,提高决策树模型M的分类性能。Step 6: Set the target loss function, place all the hyperparameters of the decision tree in the network, and use the grid search algorithm to adjust the decision tree parameters to optimize the classification of the decision tree model M. At the same time, in order to avoid the impact of the division of the training set and the test set, the data cross-validation method is used to improve the classification performance of the decision tree model M.
3.按照权利要求1所述的一种航空飞行器异常飞行状态智能识别方法,其特征在于步骤5)具体包括以下步骤:3. The method for intelligently identifying abnormal flight status of an aircraft according to claim 1, wherein step 5) specifically comprises the following steps: 步骤1:对已标注好飞行阶段的X,依据表1计算各飞行阶段的异常特征参数值,各飞行阶段的特征参数集合记为S;Step 1: For X with marked flight stages, calculate the abnormal characteristic parameter values of each flight stage according to Table 1, and the characteristic parameter set of each flight stage is recorded as S; 起飞、爬升、平飞、转弯、下降和着陆共6个最基本飞行阶段为例,每个飞行阶段可以用飞行速度、高度、姿态角和航向角等特征参数进行表征,同时每个飞行阶段都对应不同的飞行控制律,建立飞行阶段的异常特征参数;Taking the six most basic flight stages of takeoff, climb, level flight, turn, descent and landing as examples, each flight stage can be characterized by characteristic parameters such as flight speed, altitude, attitude angle and heading angle. At the same time, each flight stage corresponds to a different flight control law, and the abnormal characteristic parameters of the flight stage are established; 其中,异常特征参数归类为:姿态异常、速度异常、航迹异常和执行机构控制异常,各类参数的计算方法如下:Among them, the abnormal characteristic parameters are classified into: abnormal attitude, abnormal speed, abnormal track and abnormal actuator control. The calculation methods of various parameters are as follows: (1)姿态异常特征参数(1) Posture abnormality characteristic parameters 航空飞行器飞行姿态异常是指姿态角(俯仰角θ、偏航角ψ、滚转角φ)的测量值较长时间偏离给定值(给定俯仰角θ_ref、给定偏航角ψ_ref、给定滚转角φ_ref),计算个N时刻点各姿态角的偏差均值(E(Δθ)、E(Δψ)和E(Δφ))作为姿态异常特征参数,即The abnormal flight attitude of an aircraft refers to the deviation of the measured values of the attitude angles (pitch angle θ, yaw angle ψ, roll angle φ) from the given values (given pitch angle θ_ref, given yaw angle ψ_ref, given roll angle φ_ref) for a long time. The mean deviation of each attitude angle at N time points (E(Δθ), E(Δψ) and E(Δφ)) is calculated as the characteristic parameter of the attitude abnormality, that is,
Figure FDA0004029946200000041
Figure FDA0004029946200000041
其中,θi、ψi、φi分别表示第i(i=1,2,...,N)个时刻点俯仰角、偏航角和滚转角的测量值,θ_refi、ψ_refi、φ_refi分别表示第i(i=1,2,...,N)个时刻点俯仰角、偏航角和滚转角的给定值;Wherein, θ i , ψ i , φ i represent the measured values of the pitch angle, yaw angle and roll angle at the i-th (i=1, 2, ..., N) time point, respectively, and θ_ref i , ψ_ref i , φ_ref i represent the given values of the pitch angle, yaw angle and roll angle at the i-th (i=1, 2, ..., N) time point, respectively; (2)速度异常特征参数(2) Speed anomaly characteristic parameters 航空飞行器速度异常是指飞行速度的测量值V较长时间偏离给定速度值V_ref,计算个N时刻点速度的偏差均值E(ΔV)作为速度异常特征参数,即The speed anomaly of an aircraft refers to the fact that the measured value V of the flight speed deviates from the given speed value V_ref for a long time. The mean deviation of the speed at N time points E(ΔV) is calculated as the speed anomaly characteristic parameter, that is,
Figure FDA0004029946200000042
Figure FDA0004029946200000042
其中,Vi、V_refi分别表示第i(i=1,2,...,N)个时刻点航空飞行器飞行速度测量值和给定值;Wherein, V i and V_ref i represent the measured value and given value of the flight speed of the aircraft at the i-th (i=1, 2, ..., N) time point, respectively; (3)航迹异常特征参数(3) Track anomaly characteristic parameters 航空飞行器航迹异常是指实际飞行航线偏离设定航线,航线是由若干航点构成,计算N个时刻点航点的距离偏差均值E(Δd)作为航迹异常特征参数,即The abnormal flight path of an aircraft refers to the deviation of the actual flight route from the set route. The route is composed of several waypoints. The mean distance deviation E(Δd) of the waypoints at N time points is calculated as the characteristic parameter of the abnormal flight path, that is,
Figure FDA0004029946200000051
Figure FDA0004029946200000051
其中,第i(i=1,2,...,N)时刻的航点位置经度、纬度和高度的测量值和给定值分别表示为Pi(xi,yi,hi)和P_refi(x_refi,y_refi,h_refi);Wherein, the measured values and given values of the longitude, latitude and altitude of the waypoint position at the i-th (i=1, 2, ..., N) moment are expressed as Pi ( xi , yi , hi ) and P_refi ( x_refi , y_refi , h_refi ), respectively; 计算N个时刻点航点的航向角偏差均值E(ΔPSI)作为航迹异常特征参数,即Calculate the mean value of the heading angle deviation E(ΔPSI) of the waypoints at N time points as the track anomaly characteristic parameter, that is,
Figure FDA0004029946200000052
Figure FDA0004029946200000052
其中,PSIi、PSI_refi分别表示第i(i=1,2,...,N)时刻航向角的测量值和给定值;Wherein, PSI i and PSI_ref i represent the measured value and given value of the heading angle at the i-th (i=1, 2, ..., N) moment respectively; (4)执行机构控制异常(4) Abnormal actuator control 航空飞行器执行机构控制异常是指执行机构位移(副翼位移dtx、方向舵位移dty、升降舵位移dtz、襟副翼位移dtjy、前轮位移dtw)偏离执行机构指令(副翼指令dtxc、方向舵指令dtyc、升降舵指令dtzc、襟副翼指令dtjyc、前轮指令dtwc),计算个N时刻点各执行机构指令的偏差均值(E(Δdtx)、E(Δdty)、E(Δdtz)、E(Δdtjy)和E(Δdtw))作为执行机构控制异常特征参数,即The abnormal control of the actuator of an aircraft refers to the deviation of the actuator displacement (aileron displacement dtx, rudder displacement dty, elevator displacement dtz, flaperon displacement dtjy, and front wheel displacement dtw) from the actuator command (aileron command dtxc, rudder command dtyc, elevator command dtzc, flaperon command dtjyc, and front wheel command dtwc). The deviation mean of the actuator commands at N time points (E(Δdtx), E(Δdty), E(Δdtz), E(Δdtjy), and E(Δdtw)) is calculated as the characteristic parameter of the actuator control abnormality, that is,
Figure FDA0004029946200000053
Figure FDA0004029946200000053
其中,dtxi、dtyi、dtzi、dtjyi、dtwi分别表示第i(i=1,2,...,N)个时刻点副翼位移、方向舵位移、升降舵位移、襟副翼位移、前轮位移的测量值,dtxci、dtyci、dtzci、dtjyci、dtwci分别表示第i(i=1,2,...,N)个时刻点副翼指令、方向舵指令、升降舵指令、襟副翼指令和前轮指令的给定值;Wherein, dtx i , dty i , dtz i , dtjy i , dtw i represent the measured values of aileron displacement, rudder displacement, elevator displacement, flaperon displacement, and nosewheel displacement at the i-th (i=1, 2, ..., N) time point, respectively; dtxc i , dtyc i , dtzc i , dtjyc i , dtwc i represent the given values of aileron command, rudder command, elevator command, flaperon command, and nosewheel command at the i-th (i=1, 2, ..., N) time point, respectively; 步骤2:从异常特征参数集合S中根据飞行专家经验选取异常影响最大的特征参数及选取最接近均值的数据点作为树的根节点;Step 2: Select the feature parameter with the greatest abnormal impact from the abnormal feature parameter set S according to the flight expert experience and select the data point closest to the mean as the root node of the tree; 步骤3:将异常特征参数值小于当前分割点的设置为二叉树的左节点Left,大于等于的设置为二叉树的右节点Right;Step 3: Set the abnormal feature parameter value that is less than the current split point as the left node of the binary tree, and set the abnormal feature parameter value that is greater than or equal to the current split point as the right node of the binary tree; 步骤4:节点Left和Right重复步骤2和步骤3递归构造树,直到只有一个数据无法继续构造,或者树已达到限定高度,停止构造;Step 4: Repeat steps 2 and 3 for nodes Left and Right to recursively construct the tree until only one data cannot be constructed further, or the tree has reached the specified height, and then stop constructing; 步骤5:计算样本x的异常分数s(x,n):Step 5: Calculate the anomaly score s(x,n) of sample x:
Figure FDA0004029946200000061
Figure FDA0004029946200000061
式中:h(x)样本x所在树的路径长度,c(n)为每棵树的路径平均长度:Where: h(x) is the path length of the tree where sample x is located, and c(n) is the average path length of each tree:
Figure FDA0004029946200000062
Figure FDA0004029946200000062
式中:H(n-1)=ln(n-1)+ζ(ζ为欧拉常数,取值0.58),对于异常分数s(x,n)接近1的数据样本x为异常数据点。In the formula: H(n-1)=ln(n-1)+ζ (ζ is the Euler constant, with a value of 0.58). The data sample x whose anomaly score s(x,n) is close to 1 is an abnormal data point.
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