CN111047916B - A heavy landing risk identification method based on the area characteristic of QAR curve - Google Patents

A heavy landing risk identification method based on the area characteristic of QAR curve Download PDF

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CN111047916B
CN111047916B CN201911395844.XA CN201911395844A CN111047916B CN 111047916 B CN111047916 B CN 111047916B CN 201911395844 A CN201911395844 A CN 201911395844A CN 111047916 B CN111047916 B CN 111047916B
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綦麟
郑林江
刘柳
廖字文
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Sichuan Hantai Technology Co ltd
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Abstract

本发明公开了一种基于QAR曲线面积特征的重着陆风险识别方法,所识别方法具体为:S1:提取单一飞机所有航段的风险识别所需的QAR参数;S2:对提取的QAR参数进行数据清洗;S3:基于S2,提取所有航段的着陆阶段的QAR数据;S4:根据所有航段着陆阶段的固定下降距离的垂直速度均值,构建均值曲线,然后提取每个航段的曲线面积特征;S5:构建损失函数,结合每一个航段的曲线面积特征,确定曲线面积特征阈值;S6:将每一个航段的曲线面积特征与曲线面积特征阈值进行对比,若大于,则认为存在重着陆风险,反之认为安全。本发明通过建立垂直速度均值曲线,通过构建损失函数,定义了损失值的计算方式,进而获得了重着陆风险识别阈值。

Figure 201911395844

The invention discloses a hard landing risk identification method based on a QAR curve area feature. The identification method is specifically: S1: extracting QAR parameters required for risk identification of all flight segments of a single aircraft; S2: performing data analysis on the extracted QAR parameters Cleaning; S3: Based on S2, extract the QAR data of the landing stage of all flight segments; S4: According to the vertical velocity average value of the fixed descent distance of all flight segments during the landing stage, construct the mean curve, and then extract the curve area feature of each flight segment; S5: Construct a loss function, and combine the curve area features of each flight segment to determine the curve area feature threshold; S6: Compare the curve area feature of each flight segment with the curve area feature threshold, if it is greater than that, it is considered that there is a risk of heavy landing , otherwise considered safe. The invention defines the calculation method of the loss value by establishing the vertical velocity mean value curve and the loss function, thereby obtaining the heavy landing risk identification threshold.

Figure 201911395844

Description

一种基于QAR曲线面积特征的重着陆风险识别方法A heavy landing risk identification method based on the area characteristic of QAR curve

技术领域technical field

本发明涉及民用客机重着陆风险研究领域,具体的,涉及一种基于QAR曲线面积特征的重着陆风险识别方法。The invention relates to the field of heavy-landing risk research of civil passenger aircraft, in particular to a heavy-landing risk identification method based on the area characteristic of a QAR curve.

背景技术Background technique

根据波音公司1959~2016年重大飞行安全事故数据显示,进近和着陆阶段是最容易发生重大安全事故的飞行阶段,事故及不安全事件的发生率明显高于其他飞行阶段。着陆阶段平均只占飞行时间的1%,但其事故发生率却高达24%。According to Boeing's major flight safety accident data from 1959 to 2016, the approach and landing stages are the flight stages most prone to major safety accidents, and the incidence of accidents and unsafe events is significantly higher than other flight stages. The landing phase averages only 1% of flight time, but its accident rate is as high as 24%.

在着陆阶段的安全事件中,重着陆是其中一类发生频繁的不安全事件,2006~2011年我国民航共发生重着陆不安全事件125起,约占着陆阶段不安全事件总数的20%。作为一类风险事件,重着陆不仅会给乘客带来不好的飞行体验,损害航空公司形象,重着陆频发会加速机翼、起落架、发动机结构的疲劳损伤甚至断裂,增大着陆安全事故的发生几率,给航空公司带来巨大经济损失,情况严重时会引发灾难性事故后果,对旅客生命安全造成威胁。Among the safety incidents in the landing stage, heavy landing is one of the frequent unsafe incidents. From 2006 to 2011, there were 125 unsafe heavy landing incidents in civil aviation in my country, accounting for about 20% of the total unsafe incidents in the landing stage. As a type of risk event, a heavy landing will not only bring a bad flight experience to passengers, but also damage the airline's image. Frequent heavy landings will accelerate the fatigue damage or even breakage of the wing, landing gear, and engine structure, and increase landing safety accidents. The probability of occurrence of the accident will bring huge economic losses to airlines, and in severe cases, it will lead to catastrophic accident consequences, threatening the life safety of passengers.

发明内容SUMMARY OF THE INVENTION

有鉴于此,本发明的目的是提供一种基于QAR曲线面积特征的重着陆风险识别方法,本方法稳定性较高,适合在本领域进行推广。In view of this, the purpose of the present invention is to provide a method for identifying the risk of heavy landing based on the area characteristic of the QAR curve. The method has high stability and is suitable for promotion in this field.

本发明的目的是通过以下技术方案实现的:The purpose of this invention is to realize through the following technical solutions:

一种基于QAR曲线面积特征的重着陆风险识别方法,所述识别方法具体为:S1:提取单一飞机所有航段的风险识别所需的QAR参数;A heavy landing risk identification method based on QAR curve area characteristics, the identification method is specifically: S1: extracting QAR parameters required for risk identification of all flight segments of a single aircraft;

S2:对提取的QAR参数进行数据清洗;S2: perform data cleaning on the extracted QAR parameters;

S3:基于S2,提取所有航段的着陆阶段的QAR数据;S3: Based on S2, extract the QAR data of the landing phase of all flight segments;

S4:根据所有航段着陆阶段的固定下降距离的垂直速度均值,构建均值曲线,然后提取每个航段的曲线面积特征;S4: According to the vertical velocity mean value of the fixed descent distance during the landing phase of all segments, construct the mean value curve, and then extract the curve area feature of each segment;

S5:构建损失函数,结合每一个航段的曲线面积特征,确定曲线面积特征阈值;S5: Construct a loss function, and combine the curve area characteristics of each flight segment to determine the curve area characteristic threshold;

S6:将每一个航段的曲线面积特征与曲线面积特征阈值进行对比,若大于曲线面积特征阈值则认为存在重着陆风险,反之认为安全。S6: Compare the curve area feature of each flight segment with the curve area feature threshold. If it is greater than the curve area feature threshold, it is considered that there is a risk of heavy landing, otherwise, it is considered safe.

进一步,所述S1具体为:Further, the S1 is specifically:

S11:对民用航空器中所有航段的QAR参数进行译码解析,得到所有航段的CSV文件;S11: Decode and parse the QAR parameters of all flight segments in the civil aircraft to obtain CSV files of all flight segments;

S12:提取所有航段的CSV文件的风险识别所需参数数据,所述参数数据包括飞机的纵向载荷、无线电高度、发动机转速、纵向加速度、空速、地速、垂直速度、襟翼状态、缝翼状态、起落架状态、扰流板状态、真实高度、俯仰角。S12: Extract the parameter data required for risk identification of the CSV file of all flight segments, the parameter data includes the aircraft's longitudinal load, radio altitude, engine speed, longitudinal acceleration, airspeed, ground speed, vertical speed, flap state, seam Wing status, landing gear status, spoiler status, true altitude, pitch angle.

进一步,所述S3具体为:Further, the S3 is specifically:

S31:针对所述参数数据的取值对飞行阶段进行划分,提取着陆阶段数据;S31: Divide the flight stage according to the value of the parameter data, and extract the landing stage data;

S32:在着陆阶段数据中,通过起落架状态参数识别飞机着落时间点;S32: In the landing stage data, identify the landing time point of the aircraft through the landing gear state parameters;

S33:在所有航段的着陆阶段数据中,提取无线电高度为50ft的时间点tstart,通过起落架状态、纵向加速度、扰流板状态参数判断出接地时间点,记为tendS33: In the landing phase data of all flight segments, extract the time point t start when the radio altitude is 50ft, and determine the touchdown time point through the parameters of the landing gear state, longitudinal acceleration, and spoiler state, and record it as t end ;

S34:提取所有的CSV文件中[tstart,tend]时间段内的所有时间点的垂直速度。S34: Extract the vertical velocity at all time points in the [t start , t end ] time period in all the CSV files.

进一步,所述S4具体为:Further, the S4 is specifically:

S41:根据所述S34,运用B样条插值法将所有航段的每一个[tstart,tend]时间段内的垂直速度点扩充至至少50个,使得垂直速度点与飞行高度相对应,所有航段均生成各自的单航段垂直速度曲线;S41: According to the S34, use the B-spline interpolation method to expand the vertical velocity points in each [t start , t end ] time period of all flight segments to at least 50, so that the vertical velocity points correspond to the flight height, All flight segments generate their own single-segment vertical speed curve;

S42:计算所有航段的每一个[tstart,tend]时间段内的每1ft的垂直速度的均值,形成垂直速度均值曲线,所述垂直速度均值曲线为纵坐标表示飞行高度,横坐标表示垂直速度均值;S42: Calculate the average value of the vertical speed per 1ft in each [t start , t end ] time period of all flight segments to form a vertical speed average curve, where the ordinate represents the flight altitude, and the abscissa represents the flight height mean vertical velocity;

S43:对比所有的单航段垂直速度曲线与垂直速度均值曲线,另位于垂直速度均值曲线上方的区域面积为正值面积,位于垂直速度均值曲线下方的区域面积为负值面积,计算每一个航段的所述曲线面积特征;S43: Compare the vertical speed curves and the average vertical speed curves of all single flight segments. The area above the average vertical speed curve is a positive area, and the area below the average vertical speed curve is a negative area. Calculate the area of each flight. said curve area characteristic of the segment;

其中曲线面积特征=正值面积-负值面积。Wherein the curve area feature = positive value area - negative value area.

进一步,所述S5具体为:Further, the S5 is specifically:

S51:构建损失函数,其中所述损失函数定义为:S51: Construct a loss function, wherein the loss function is defined as:

损失值=a×假反例率+b×假正例率;Loss value = a × false negative rate + b × false positive rate;

其中:假反例率=判断为反例实际为正例的数量/判断为反例的总数量,假正例率=判断为正例实际为反例的数量/判断为正例的总数量;Among them: the rate of false negative examples = the number of positive examples that are judged to be negative examples/the total number of positive examples that are judged to be negative examples, the rate of false positive examples = the number of positive examples that are judged to be actually negative examples/the total number of positive examples that are judged to be positive;

a和b分别为根据对应误判类型的严重程度决定的系数;a and b are the coefficients determined according to the severity of the corresponding misjudgment type, respectively;

S52:设定不同的阈值,依据所述曲线面积特征是否超过阈值进行正反例的判断,超过阈值则认为正例,未超过认为反例,该步骤为实际判断正、反利的方法。S52: Set different thresholds, and judge the positive and negative examples according to whether the curve area feature exceeds the threshold. If the threshold is exceeded, it is considered as a positive example, and if it does not exceed the threshold, it is considered as a negative example. This step is a method for actually judging positive and negative benefits.

S53:根据损失函数,计算不同阈值下的损失值,取损失值最小时的阈值作为此特征重着陆风险识别的阈值。S53: Calculate loss values under different thresholds according to the loss function, and take the threshold with the smallest loss value as the threshold for re-landing risk identification of this feature.

本发明的有益效果是:The beneficial effects of the present invention are:

本发明基于飞机的QAR数据,通过建立垂直速度均值曲线,使得单一航段的垂直速度曲线与其进行对比并获得曲线面积特征,然后通过构建损失函数,定义了损失值的计算方式,进而获得了重着陆风险识别阈值,本方法稳定性较高,适合在本领域进行推广。Based on the QAR data of the aircraft, the present invention establishes a vertical speed mean curve, so that the vertical speed curve of a single flight segment is compared with it and obtains the curve area characteristics, and then by constructing a loss function, the calculation method of the loss value is defined, and the weight value is obtained. The landing risk identification threshold, the method has high stability and is suitable for promotion in this field.

本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述,并且在某种程度上,基于对下文的考察研究对本领域技术人员而言将是显而易见的,或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.

附图说明Description of drawings

为了使本发明的目的、技术方案和优点更加清楚,下面将结合附图对本发明作进一步的详细描述,其中:In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings, wherein:

附图1为本发明流程图;Accompanying drawing 1 is the flow chart of the present invention;

附图2为样本曲线面积图。Accompanying drawing 2 is the sample curve area graph.

具体实施方式Detailed ways

以下将参照附图,对本发明的优选实施例进行详细的描述。应当理解,优选实施例仅为了说明本发明,而不是为了限制本发明的保护范围。The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

实施例1Example 1

本实施例提出了一种基于QAR曲线面积特征的重着陆风险识别方法,识别方法具体为This embodiment proposes a hard landing risk identification method based on the area feature of the QAR curve. The identification method is specifically as follows:

S1:提取所有航段的风险识别所需的QAR参数;S1: Extract the QAR parameters required for risk identification of all flight segments;

S11:对所有航段的QAR参数进行译码解析,得到所有航段的CSV文件;S11: Decode and parse the QAR parameters of all flight segments to obtain CSV files of all flight segments;

S12:提取所有航段的CSV文件的风险识别所需参数数据,参数数据包括飞机的纵向载荷、无线电高度、发动机转速、纵向加速度、空速、地速、垂直速度、襟翼状态、缝翼状态、起落架状态、扰流板状态、真实高度、俯仰角。S12: Extract the parameter data required for risk identification of the CSV file of all flight segments, the parameter data includes the aircraft's longitudinal load, radio altitude, engine speed, longitudinal acceleration, airspeed, ground speed, vertical speed, flap status, slat status , Landing gear status, spoiler status, true altitude, pitch angle.

S2:对提取的QAR参数进行数据清洗;S2: perform data cleaning on the extracted QAR parameters;

原始QAR数据由于译码错位或采集误差等因素,会存在部分数据字段错位或信息缺失等明显异常情况。结合异常数据所处时间点附近一段时间内,飞机状态的所有参数数据,对异常数据进行识别、删除和推断补全。Due to factors such as decoding misalignment or acquisition error, the original QAR data may have obvious abnormalities such as misalignment of some data fields or missing information. Combined with all the parameter data of the aircraft state for a period of time near the time point where the abnormal data is located, the abnormal data is identified, deleted, and inferred and completed.

异常数据识别范围:CSV文件不完整,没有从起飞到着陆的全过程;CSV文件为出发地和目的地都相同的飞行训练数据;译码输出的CSV文件参数错位,即在参数1那一列中的某一行,显示参数2的数据;参数取值超出理论取值范围;参数取值出现不合逻辑的跳变等。Abnormal data identification range: The CSV file is incomplete, and there is no whole process from take-off to landing; the CSV file is the flight training data with the same departure and destination; the parameters of the CSV file output by decoding are misplaced, that is, in the parameter 1 column A row of , displays the data of parameter 2; the parameter value exceeds the theoretical value range; the parameter value has an illogical jump, etc.

删除操作:对于上述提到的CSV文件格式异常情况,视作无效数据而弃用;对于CSV文件本身格式正确,仅是参数取值偶有异常的数据,仅删去CSV文件中的异常数据,之后结合其他参数推断补全。Delete operation: For the abnormal situation of the CSV file format mentioned above, it is regarded as invalid data and discarded; for the data of the correct format of the CSV file itself, only the parameter values are occasionally abnormal, only the abnormal data in the CSV file is deleted. Completion is then inferred in combination with other parameters.

推断补全的方法:对于速度、经纬度、高度等连续数值类参数,一般取前后平均值;对于襟翼状态、缝翼状态等离散的状态类参数,一般取前值或后值填充。The method of inference and completion: For continuous numerical parameters such as speed, latitude and longitude, and altitude, the average value of the front and rear is generally taken; for discrete state parameters such as flap status and slat status, the front value or the rear value is generally used for filling.

S3:如图1所示,基于S2,提取所有航段的着陆阶段的QAR数据;S3: As shown in Figure 1, based on S2, extract the QAR data of the landing phase of all flight segments;

S31:针对参数数据的取值对所有航段的飞行阶段进行划分,提取着陆阶段数据;S31: Divide the flight stages of all flight segments according to the value of the parameter data, and extract the landing stage data;

S32:在着陆阶段数据中,通过起落架状态参数识别飞机着落时间点,即仅采用CSV文件中着陆阶段对应的那些行的参数数据;S32: In the landing stage data, identify the landing time point of the aircraft through the landing gear state parameters, that is, only use the parameter data of those lines corresponding to the landing stage in the CSV file;

S33:在每一个航段的着陆阶段数据中,提取无线电高度为50ft的时间点tstart,通过起落架状态、纵向加速度、扰流板状态参数判断出对应航段的接地时间点,记为tendS33: In the landing phase data of each flight segment, extract the time point t start when the radio altitude is 50ft, and judge the touchdown time point of the corresponding flight segment through the parameters of the landing gear state, longitudinal acceleration, and spoiler state, which is recorded as t end ;

飞机接地时间点的判定方法为:The method of determining the time point of the aircraft touchdown is as follows:

分别提取着陆阶段数据的无线电高度、起落架空地电门状态、扰流板位置、纵向加速度和无线电高度五种类型参数的最高频率数据;Extract the highest frequency data of five types of parameters including radio altitude, landing gear air-ground switch status, spoiler position, longitudinal acceleration and radio altitude of the landing stage data respectively;

将五种类型参数的除最高频率数据外的其他数据分别处理为与对应的最高频率数据相同的频率,这些数据的频率从一秒一次到一秒八次不等,因此需要提升低频数据的频率至与最高频率数据一致,保证接地时间点的精确度更高。The data of the five types of parameters except the highest frequency data are processed to the same frequency as the corresponding highest frequency data. The frequency of these data ranges from once per second to eight times per second, so it is necessary to increase the frequency of low-frequency data. To be consistent with the highest frequency data, the accuracy of the grounding time point is guaranteed to be higher.

对于不同数据采用不同的升频处理方法,如:起落架空地电门状态采用前值填充;扰流板位置采用线性插值(前后均值);纵向加速度采用垂直速度推算每帧数据所占比例,然后按比例分配;无线电高度采用垂直速度计算结合二次样条插值的方法。Different upscaling processing methods are used for different data, such as: the state of the landing gear air-ground switch is filled with the previous value; the position of the spoiler is linearly interpolated (the average value of front and rear); the longitudinal acceleration is calculated by using the vertical speed to calculate the proportion of each frame of data, and then Proportional allocation; radio heights are calculated using vertical velocity combined with quadratic spline interpolation.

以决策条件为依据,判定飞机的接地时间。具体为:Based on the decision-making conditions, determine the grounding time of the aircraft. Specifically:

从着陆阶段开始之后,找到无线电高度小于3的第一个时间点tstart,作为循环判断开始的时间起点;从tstart开始往后遍历每一个时间点,直到遇到一个满足决策条件中任意一个的点,将该点标记为接地点tTD并输出。其中,决策条件包括第一条件、第二条件和第三条件,满足任一条件,即为满足决策条件,其中第一条件为:任意从tstart往后遍历的时间点的扰流板位置较上一时间点的扰流板位置发生大于突变值I的变化,突变值I为4-6;第二条件为:任意从tstart往后遍历的时间点的纵向加速度较上一时间点的纵向加速度发生大于突变值II的变化,突变值II为0.025-0.035;第三条件为:任一从tstart往后遍历的时间点发生起落架空地电门状态转换。After the start of the landing phase, find the first time point t start with the radio height less than 3, as the time starting point for the loop judgment; traverse each time point from t start until it encounters any one of the decision conditions. , mark the point as the ground point t TD and output. Among them, the decision-making conditions include the first condition, the second condition and the third condition, and satisfying any one of the conditions is the decision-making condition, wherein the first condition is: the spoiler position at any time point traversed from t start to the back is relatively The position of the spoiler at the last time point has a change greater than the sudden change value I, and the sudden change value I is 4-6; the second condition is: the longitudinal acceleration of any time point traversed from t start onward is higher than the longitudinal acceleration of the previous time point. The acceleration is larger than the sudden change value II, and the sudden change value II is 0.025-0.035; the third condition is: the state transition of the landing gear air-ground switch occurs at any time point traversed from t start to the back.

S34:提取每一个航段对应的着陆时间段为[tstart,tend]内的所有时间点的垂直速度。S34: Extract the vertical speed at all time points within [t start , t end ] in the landing time period corresponding to each flight segment.

S4:根据所有航段着陆阶段的固定下降距离的垂直速度均值,构建均值曲线,然后提取每个航段的曲线面积特征;S4: According to the vertical velocity mean value of the fixed descent distance during the landing phase of all segments, construct the mean value curve, and then extract the curve area feature of each segment;

S41:根据所述S34,运用B样条插值法将每一个航段的[tstart,tend]时间段内的垂直速度点扩充至50个,使得垂直速度点与飞行高度相对应,所有航段均生成各自的单航段垂直速度曲线;S41: According to the S34, use the B-spline interpolation method to expand the vertical speed points in the [t start , t end ] time period of each flight segment to 50, so that the vertical speed points correspond to the flight altitude, and all flight Each segment generates its own single-segment vertical speed curve;

B样条插值法可以根据已知的垂直速度与对应的时间点,生成一条平滑的函数关系曲线,并通过该函数关系曲线,将该时间段内的垂直速度点扩充至50个,使得垂直速度与飞机飞行高度相对应,The B-spline interpolation method can generate a smooth function relationship curve according to the known vertical speed and the corresponding time point, and through the function relationship curve, the vertical speed points in the time period can be expanded to 50, so that the vertical speed Corresponding to the flight altitude of the aircraft,

S42:计算所有航段的每一个[tstart,tend]时间段内的每1ft的垂直速度的均值,形成垂直速度均值曲线,垂直速度均值曲线为纵坐标表示飞行高度,横坐标表示垂直速度均值;S42: Calculate the average value of the vertical speed per 1ft in each [t start , t end ] time period of all flight segments, and form a vertical speed average curve. The vertical speed average curve is that the ordinate represents the flight altitude, and the abscissa represents the vertical speed mean;

S43:对比所有的单航段垂直速度曲线与垂直速度均值曲线,另位于垂直速度均值曲线上方的区域面积为正值面积,位于垂直速度均值曲线下方的区域面积为负值面积,计算每一个航段的曲线面积特征;S43: Compare the vertical speed curves and the average vertical speed curves of all single flight segments. The area above the average vertical speed curve is a positive area, and the area below the average vertical speed curve is a negative area. Calculate the area of each flight. Curve area feature of the segment;

其中曲线面积特征=正值面积-负值面积。Wherein the curve area feature = positive value area - negative value area.

S5:构建损失函数,结合每一个航段的曲线面积特征,确定曲线面积特征阈值。S5: Construct a loss function, and combine the curve area characteristics of each flight segment to determine the curve area characteristic threshold.

S51:构建损失函数,其中损失函数定义为:S51: Construct a loss function, where the loss function is defined as:

损失值=a×假反例率+b×假正例率;Loss value = a × false negative rate + b × false positive rate;

其中:假反例率=初步判断为反例实际为正例的数量/判断为反例的总数量,假正例率=初步判断为正例实际为反例的数量/判断为正例的总数量;Among them: the rate of false negative examples = the number of positive examples that are initially judged to be negative examples/the total number of positive examples that are judged to be negative examples, and the rate of false positive examples = the number of positive examples that are initially judged to be negative examples/the total number of positive examples that are judged to be positive examples;

其中初步判断正、反例的方法为:The methods for judging positive and negative examples are as follows:

提取参数数据中的着陆载荷,其中:Extract landing loads from parametric data, where:

若着陆载荷>1.5的航段,则认为有着陆风险,即为正例,反之为反例。If the flight segment with the landing load > 1.5, it is considered that there is a landing risk, which is a positive example, otherwise it is a negative example.

a和b分别为根据对应误判类型的严重程度决定的系数;a and b are the coefficients determined according to the severity of the corresponding misjudgment type, respectively;

S52:设定不同的阈值,依据所述曲线面积特征是否超过阈值进行正反例的判断,超过阈值则认为正例,未超过认为反例;S52: Set different thresholds, and judge the positive and negative examples according to whether the curve area feature exceeds the threshold value, and if it exceeds the threshold value, it is considered as a positive example, and if it does not exceed the threshold, it is considered as a negative example;

S53:根据损失函数,计算不同阈值下的损失值,取损失值最小时的阈值作为此特征重着陆风险识别的阈值。S53: Calculate loss values under different thresholds according to the loss function, and take the threshold with the smallest loss value as the threshold for re-landing risk identification of this feature.

之后可将此模型应用到其他航段数据(非样本数据)中,作为识别重着陆风险的特征。具体操作为,计算新样例的曲线面积特征值,比较其与阈值的关系,若大于阈值则认为存在重着陆风险,反之认为安全。This model can then be applied to other flight segment data (non-sample data) as a feature to identify the risk of a hard landing. The specific operation is to calculate the characteristic value of the curve area of the new sample, and compare its relationship with the threshold value. If it is greater than the threshold value, it is considered that there is a risk of heavy landing, otherwise it is considered safe.

实施例2Example 2

本模型在实际运用过程中使用了超过20000条数据用以拟合,以其中一条样本数据为例,提取出其着陆阶段垂直速度数据如表1所示:In the actual application process of this model, more than 20,000 pieces of data are used for fitting. Taking one of the sample data as an example, the vertical speed data of the landing stage are extracted as shown in Table 1:

表1样本数据Table 1 Sample data

Figure BDA0002346280940000061
Figure BDA0002346280940000061

根据实施例1所述的接地时间点判定方法,tstart具体为21.75秒,tend为30.5秒,灰色是取包含这个时间点的最近两个整数时间点组成的时间段。According to the method for judging the grounding time point described in Embodiment 1, t start is specifically 21.75 seconds, t end is 30.5 seconds, and the gray color is a time period consisting of the nearest two integer time points including this time point.

对tstart到tend区间内的数据进行B样条插值,得到垂直速度数据作为特征向量,数据如下:B-spline interpolation is performed on the data in the interval from t start to t end , and the vertical velocity data is obtained as a feature vector. The data are as follows:

[-651.33,-654.81,-656.86,-657.13,-655.71,-652.71,-648.23,-642.37,-635.25,-626.86,-617.15,-606.07,-593.54,-579.52,-564.0,-547.37,-530.22,-513.16,-496.77,-481.67,-468.2,-455.46,-442.05,-426.59,-407.71,-384.0,-354.75,-321.89,-287.99,-255.64,-227.41,-205.9,-192.65,-186.3,-184.98,-186.81,-189.92,-192.45,-193.15,-192.06,-189.38,-185.29,-179.99,-173.67,-166.88,-160.69,-156.22,-154.56,-156.83,-164.09][-651.33,-654.81,-656.86,-657.13,-655.71,-652.71,-648.23,-642.37,-635.25,-626.86,-617.15,-606.07,-593.54,-579.52,-564.0,-547.37 530.22,-513.16,-496.77,-481.67,-468.2,-455.46,-442.05,-426.59,-407.71,-384.0,-354.75,-321.89,-287.99,-255.64,-227.41,-5,-205.9,- -186.3, -184.98, -186.81, -189.92, -192.45, -193.15, -192.06, -189.38, -185.29, -179.99, -173.67, -166.88, -160.69, -156.22, -154.56, -156.83 ]

反复上述操作,计算所有样本特征向量后,计算出均值向量,样本和均值的数据如表2:Repeat the above operations, after calculating all the sample feature vectors, calculate the mean vector, and the data of the sample and mean are shown in Table 2:

表2样本和均值数据Table 2 Sample and mean data

SAMPLESAMPLE MEANMEAN 11 -651.325-651.325 -745.227-745.227 22 -654.812-654.812 -744.545-744.545 33 -656.864-656.864 -744.042-744.042 44 -657.134-657.134 -743.565-743.565 55 -655.713-655.713 -743.229-743.229 66 -652.708-652.708 -743.225-743.225 77 -648.226-648.226 -742.053-742.053 88 -642.373-642.373 -740.15-740.15 99 -635.249-635.249 -737.825-737.825 1010 -626.862-626.862 -735.542-735.542 1111 -617.153-617.153 -733.083-733.083 1212 -606.066-606.066 -730.075-730.075 1313 -593.541-593.541 -727.254-727.254 1414 -579.522-579.522 -725.014-725.014 1515 -563.999-563.999 -723.034-723.034 1616 -547.367-547.367 -721.284-721.284 1717 -530.222-530.222 -719.316-719.316 1818 -513.159-513.159 -716.87-716.87 1919 -496.775-496.775 -714.035-714.035 2020 -481.666-481.666 -709.201-709.201 21twenty one -468.202-468.202 -699.505-699.505 22twenty two -455.459-455.459 -679.604-679.604 23twenty three -442.051-442.051 -642.977-642.977 24twenty four -426.595-426.595 -584.347-584.347 2525 -407.706-407.706 -505.942-505.942 2626 -384-384 -417.118-417.118 2727 -354.754-354.754 -332.954-332.954 2828 -321.889-321.889 -265.253-265.253 2929 -287.988-287.988 -219.815-219.815 3030 -255.635-255.635 -195.643-195.643 3131 -227.412-227.412 -153.816-153.816 3232 -205.901-205.901 -120.611-120.611 3333 -192.651-192.651 -127.079-127.079 3434 -186.301-186.301 -78.3901-78.3901 3535 -184.977-184.977 -53.0421-53.0421 3636 -186.808-186.808 -40.3578-40.3578 3737 -189.92-189.92 -41.8736-41.8736 3838 -192.445-192.445 -44.5733-44.5733 3939 -193.147-193.147 -43.1673-43.1673 4040 -192.061-192.061 -43.3547-43.3547 4141 -189.379-189.379 -42.1593-42.1593 4242 -185.292-185.292 -41.3004-41.3004 4343 -179.989-179.989 -37.6459-37.6459 4444 -173.669-173.669 -34.7692-34.7692 4545 -166.881-166.881 -31.4725-31.4725 4646 -160.694-160.694 -27.5653-27.5653 4747 -156.216-156.216 -23.0006-23.0006 4848 -154.558-154.558 -17.6923-17.6923 4949 -156.829-156.829 -11.5501-11.5501 5050 -164.087-164.087 -4.32967-4.32967

建立如图2所示的曲线图,对SAMPLE-MEAN进行面积积分,计算出此样本的曲线面积特征值为853.2365075。重复上述操作计算出所有样本的曲线面积特征值。选取一定的阈值,若样本曲线面积特征大于阈值则判断样本为正例,否则判断为负例,按此方法对所有样本进行判断,对比样本实际正负例数量,计算假反例率和假正例率,并以此为基础计算此阈值对应的损失值,其中损失值=a×假反例率+b×假正例率。此例中,取a=0.75-1.25,b=1.75-2.25,改变阈值重复此操作,计算出多个阈值所对应的损失值。取最小损失值对应的阈值作为识别重着陆风险的特征阈值。此例中,最小损失值对应的阈值为10287.72。其中5个阈值对应损失值如下:Establish the curve graph as shown in Figure 2, carry out the area integration of SAMPLE-MEAN, and calculate the characteristic value of the curve area of this sample to be 853.2365075. Repeat the above operation to calculate the curve area eigenvalues of all samples. Select a certain threshold, if the area characteristic of the sample curve is greater than the threshold, the sample is judged as a positive example, otherwise it is judged as a negative example, judge all samples according to this method, compare the actual number of positive and negative examples of the sample, and calculate the false negative example rate and false positive example rate, and calculate the loss value corresponding to this threshold based on this, where loss value=a×false negative rate+b×false positive rate. In this example, take a=0.75-1.25, b=1.75-2.25, change the threshold and repeat this operation to calculate the loss values corresponding to multiple thresholds. The threshold corresponding to the minimum loss value is taken as the characteristic threshold for identifying the risk of heavy landing. In this example, the threshold corresponding to the minimum loss value is 10287.72. The corresponding loss values for the 5 thresholds are as follows:

表3table 3

Figure BDA0002346280940000081
Figure BDA0002346280940000081

上述样本曲线面积特征值为853.2365075,远小于阈值,着陆载荷为1.148,远小于重着陆风险值1.5。The characteristic value of the area of the above sample curve is 853.2365075, which is much smaller than the threshold value, and the landing load is 1.148, which is much smaller than the 1.5 risk value of heavy landing.

最后说明的是,以上实施例仅用以说明本发明的技术方案而非限制,尽管参照较佳实施例对本发明进行了详细说明,本领域的普通技术人员应当理解,可以对本发明的技术方案进行修改或者等同替换,而不脱离本技术方案的宗旨和范围,其均应涵盖在本发明的权利要求范围当中。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.

Claims (2)

1. A heavy landing risk identification method based on QAR curve area characteristics is characterized in that: the identification method specifically comprises the following steps: s1: extracting QAR parameters required by risk identification of all flight segments of a single airplane;
s2: carrying out data cleaning on the extracted QAR parameters;
s3: based on S2, extracting QAR data of landing stages of all the legs;
s4: constructing a mean value curve according to the vertical speed mean values of the fixed descending distances of all flight sections in the landing stage, and then extracting the curve area characteristics of each flight section;
s5: constructing a loss function, and determining a curve area characteristic threshold value by combining the curve area characteristic of each flight segment;
s6: comparing the curve area characteristic of each flight segment with a curve area characteristic threshold, if the curve area characteristic is larger than the curve area characteristic threshold, determining that the risk of heavy landing exists, otherwise, determining that the safety exists;
the S3 specifically includes:
s31: dividing flight phases according to the values of the parameter data, and extracting landing phase data;
s32: identifying the landing time point of the airplane through the landing gear state parameters in the landing stage data;
s33: in landing phase data of all legs, a time point t at which the radio altitude is 50ft is extractedstartJudging the grounding time point by the landing gear state, the longitudinal acceleration and the spoiler state parameter, and recording the grounding time point as tend
S34: extracting [ t ] from all CSV filesstart,tend]Vertical velocities of all time points within a time period;
the S4 specifically includes:
s41: according to the S34, each [ t ] of all the legs is interpolated by B splinesstart,tend]The number of the vertical speed points in the time period is expanded to at least 50, so that the vertical speed points correspond to the flying height, and all the flight sections generate respective single flight section vertical speed curves;
s42: calculate each of all legs [ t ]start,tend]Forming a vertical speed mean value curve by the mean value of every 1ft of vertical speed in a time period, wherein the vertical speed mean value curve is a vertical coordinate representing the flight height, and the horizontal coordinate representing the vertical speed mean value;
s43: comparing all the single-flight-section vertical speed curves with the vertical speed mean value curve, enabling the area of the area above the vertical speed mean value curve to be a positive-value area, enabling the area of the area below the vertical speed mean value curve to be a negative-value area, and calculating the curve area characteristic of each flight section;
wherein the curve area characteristic is positive value area-negative value area;
the S5 specifically includes:
s51: constructing a loss function, wherein the loss function is defined as:
the loss value is a multiplied by the false negative rate + b multiplied by the false positive rate;
wherein: the false positive rate is the number of the positive examples/the total number of the positive examples;
a and b are coefficients determined according to the severity of the corresponding misjudgment type respectively;
s52: setting different thresholds, judging positive and negative examples according to whether the curve area characteristics exceed the thresholds, and considering the positive examples and not considering the negative examples if the curve area characteristics exceed the thresholds;
s53: and calculating loss values under different thresholds according to the loss function, and taking the threshold with the minimum loss value as the threshold for identifying the characteristic re-landing risk.
2. The QAR curve area feature-based heavy landing risk identification method according to claim 1, wherein: the S1 specifically includes:
s11: decoding and analyzing QAR parameters of all flight segments in the civil aircraft to obtain CSV files of all flight segments;
s12: extracting parameter data required by risk identification of CSV files of all the flight segments, wherein the parameter data comprise longitudinal load, radio altitude, engine rotating speed, longitudinal acceleration, airspeed, ground speed, vertical speed, flap state, slat state, undercarriage state, spoiler state, true altitude and pitch angle of the airplane.
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