Methods for determining false flight path data, electronic devices and storage media

Abstract

This invention provides a method, electronic device, and storage medium for determining false flight trajectory point data, relating to the technical field of false flight trajectory point data determination. The method includes: acquiring a normalized flight data list set D; determining the flight data group corresponding to each type of normalized flight data for each trajectory point, to obtain a flight data group list set EA corresponding to D; and processing EA... j Perform linear fitting to obtain the linear regression function y(i); obtain EA. j The minimum distance between the coordinate point corresponding to each flight data set and y(i) is used to obtain EA. j The corresponding minimum distance list LT j If each minimum distance list set in LT satisfies the preset second judgment condition, then the data corresponding to the latest n flight trajectory points is determined to be false trajectory point data. This invention can prevent false flight trajectory point data from being used by the data monitoring platform, which could lead to data monitoring anomalies on the data monitoring platform.

Description

Technical Field

[0001] This invention relates to the field of false flight trajectory point data determination technology, and in particular to a method, electronic device and storage medium for determining false flight trajectory point data. Background Technology

[0002] In the civil aviation sector, airport data monitoring platforms typically use the ADS-B system to acquire real-time flight path data of aircraft, enabling real-time monitoring of each aircraft's flight information. However, the flight path data received by the ADS-B system may not be entirely accurate, as it may contain maliciously injected false flight path data. For example, false flight path data may be generated automatically by software. These false flight path data do not actually correspond to any real aircraft, and if the data monitoring platform uses this false flight path data, it will cause data monitoring anomalies. Summary of the Invention

[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0004] According to a first aspect of this application, a method for determining false flight trajectory point data is provided, the method comprising the following steps:

[0005] S100, obtain the latest normalized flight data for each type corresponding to the n flight trajectory points, to obtain a normalized flight data list set D = (D1, D2, ..., D...). j ,…,D m ), j=1,2,…,m; where, D j D represents the list of normalized flight data of type j, where m is the number of normalized flight data types; j =(D 1，j D 2，j D i，j D n，j ), i=1,2,…,n; D i，j This refers to the j-th type of normalized flight data corresponding to the i-th flight trajectory point.

[0006] S200, based on D, determine the flight data group corresponding to each type of normalized flight data for each trajectory point, so as to obtain the flight data group list set EA = (EA1, EA2, ..., EA) corresponding to D. j ,…,EA m ); where EA j D j The corresponding flight data set list; EA j = (EA) 1，j EA2，j , ..., EA i，j , ..., EA n，j ); EA i，j For the flight data set corresponding to the j-th type of normalized flight data at the i-th flight trajectory point; EA i，j = (i, D) i，j ).

[0007] S300 uses a preset linear regression fitting method to fit EA. j Perform linear fitting to obtain EA. j The corresponding linear regression function is y(i) = u × i + v; where u is the regression function for EA. j The weights of the linear regression function obtained by linear fitting, where v is the weight of EA. j The bias of the linear regression function obtained by linear fitting.

[0008] S400, Obtain EA j The minimum distance between the coordinate point corresponding to each flight data set and y(i) is used to obtain EA. j The corresponding minimum distance list LT j =(LT 1，j LT 2，j , ..., LT i，j , ..., LT n，j ); and thus obtain the minimum distance list set LT = (LT1, LT2, ..., LT) corresponding to EA. j ,…,LT m ); where LT i，j For EA i，j The minimum distance to y(i).

[0009] S500, if each minimum distance list set in LT satisfies the preset second judgment condition, then the data corresponding to the latest n flight trajectory points are determined to be false trajectory point data.

[0010] According to another aspect of this application, a non-transitory computer-readable storage medium is also provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-described method for determining false flight trajectory point data.

[0011] According to another aspect of this application, an electronic device is also provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects:

[0013] The method for determining false flight trajectory point data of the present invention constructs flight data groups from the normalized flight data corresponding to the acquired n flight trajectory points to obtain a flight data group list set EA corresponding to the normalized flight data list set. Each flight data group list in EA is fitted to obtain a linear regression function corresponding to each flight data group list. The minimum distance between each flight data group in each flight data group list and the corresponding linear regression function is obtained. If each minimum distance list set satisfies a preset second judgment condition, the data corresponding to the latest n flight trajectory points is determined to be false trajectory point data. This avoids the false flight trajectory point data being used by the data monitoring platform, thus preventing data monitoring anomalies from occurring on the data monitoring platform. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating the method for determining false flight trajectory point data provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that, based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Furthermore, this device and / or practice the method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0018] The following will refer to Figure 1 The flowchart shown illustrates a method for determining false flight trajectory point data, which introduces such a method.

[0019] The method for determining false flight path point data may include the following steps:

[0020] S100, obtain the latest normalized flight data for each type corresponding to the n flight trajectory points, to obtain a normalized flight data list set D = (D1, D2, ..., D...). j ,…,D m ), j=1,2,…,m; where, D j D represents the list of normalized flight data of type j, where m is the number of normalized flight data types; j =(D 1，j D 2，j D i，j D n，j ), i=1,2,…,n; D i，j This refers to the j-th type of normalized flight data corresponding to the i-th flight trajectory point.

[0021] In this embodiment, D can be obtained through the following steps:

[0022] S110, obtain the latest flight data corresponding to the n flight trajectory points to obtain the initial flight data list set A = (A1, A2, ..., A...). i ,…,A n ); where A i A is the initial flight data list corresponding to the i-th flight trajectory point; i = (A i，1 A i，2 A i，j A i，m A i，j The i-th flight trajectory point is the j-th type of flight data in the initial flight data list, where m is the number of flight data types; the r-th flight trajectory point is adjacent to the (r+1)-th flight trajectory point; r = 1, 2, ..., n-1.

[0023] In this embodiment, the civil aviation sector uses Automatic Dependent Surveillance-Broadcast (ADS-B) to acquire flight data corresponding to aircraft flight trajectory points within a certain area in real time. It is understood that the acquired flight data corresponding to the aircraft flight trajectory points may be generated manually, i.e., false flight data. Each flight trajectory point corresponds to j types of flight data, such as flight altitude, flight speed, longitude, or latitude. One flight trajectory point corresponds to one flight data, thus obtaining an initial flight data list. The acquired n flight trajectory points correspond to consecutive flight trajectory points of the same aircraft.

[0024] S120, Based on A, obtain the intermediate flight data list set B = (B1, B2, ..., B...) corresponding to each type of flight data.j ,…,B m ); where B j B is the intermediate flight data list corresponding to the j-th type of flight data. j = (A 1，j A 2，j A i，j A n，j ).

[0025] In this embodiment, each initial flight data list includes j types of flight data, which can acquire the same type of flight data for each flight trajectory point to obtain B; for example, the flight altitude corresponding to each flight trajectory point is put into the same intermediate flight data list to obtain the intermediate flight data list corresponding to the flight altitude.

[0026] S130, normalize the flight data in each intermediate flight data list in B to obtain the normalized flight data list set D corresponding to B.

[0027] Furthermore, step S130 may include the following steps:

[0028] S131, Obtain the preset maximum value MAX corresponding to the j-th data type. j and preset minimum value MIN j .

[0029] In this embodiment, MAX j and MIN j This can be obtained from several historical flight data of type j. It is understandable that MAX... j and MIN j It is fixed and unchanging.

[0030] S132, according to MAX j and MIN j Determine D i，j = (A i，j -MIN j ) / (MAX j -MIN j ); and thus we get D.

[0031] It should be noted that the normalization method in this embodiment is different from the normalization method in the above embodiments. In this embodiment, MAX j and MIN j It is fixed and unchanging, while B in the above embodiment is different. j，max and B j，min Each time the latest flight data corresponding to the n flight trajectory points is obtained, it may change; MAX j and MIN jSet it to a fixed value to facilitate subsequent calculations.

[0032] Furthermore, the flight data includes the time, altitude, speed, longitude, and latitude corresponding to the flight trajectory points.

[0033] S200, based on D, determine the flight data group corresponding to each type of normalized flight data for each trajectory point, so as to obtain the flight data group list set EA = (EA1, EA2, ..., EA) corresponding to D. j ,…,EA m ); where EA j D j The corresponding flight data set list; EA j = (EA) 1，j EA 2，j , ..., EA i，j , ..., EA n，j ); EA i，j For the flight data set corresponding to the j-th type of normalized flight data at the i-th flight trajectory point; EA i，j = (i, D) i，j ).

[0034] In this embodiment, the flight data set can be understood as constructed X-axis and Y-axis coordinates, for example: EA i，j = (i, D) i，j ), i is the X-axis coordinate, D i，j The Y-axis coordinate is used to ensure that each flight data set can fit the corresponding regression line. It should be noted that in this embodiment, the time interval between the generation times of flight data corresponding to two adjacent flight trajectories is fixed, for example, the time interval is 50ms.

[0035] S300 uses a preset linear regression fitting method to fit EA. j Perform linear fitting to obtain EA. j The corresponding linear regression function is y(i) = u × i + v; where u is the regression function for EA. j The weights of the linear regression function obtained by linear fitting, where v is the weight of EA. j The bias of the linear regression function obtained by linear fitting.

[0036] In this embodiment, the preset linear regression fitting method can be the least squares method. It should be noted that those skilled in the art can use existing least squares methods to fit EA according to actual needs. j Perform linear fitting to obtain EA. j The corresponding linear regression function y(i) = u×i+v will not be elaborated here.

[0037] S400, Obtain EAj The minimum distance between the coordinate point corresponding to each flight data set and y(i) is used to obtain EA. j The corresponding minimum distance list LT j =(LT 1，j LT 2，j , ..., LT i，j , ..., LT n，j ); and thus obtain the minimum distance list set LT = (LT1, LT2, ..., LT) corresponding to EA. j ,…,LT m ); where LT i，j For EA i，j The minimum distance to y(i).

[0038] In this embodiment, it should be noted that those skilled in the art can use existing point-to-line minimum distance calculation methods to calculate EA according to actual needs. j The minimum distance between the coordinate point corresponding to each flight data set and y(i) is not elaborated here.

[0039] S500, if each minimum distance list set in LT satisfies the preset second judgment condition, then the data corresponding to the latest n flight trajectory points are determined to be false trajectory point data.

[0040] Furthermore, step S500 may include the following steps:

[0041] S510, Based on LT, determine the minimum distance volatility corresponding to each minimum distance list in LT, so as to obtain the minimum distance volatility list η = (η1, η2, ..., η) corresponding to LT. j ,…,η m ); where η j For LT j The corresponding minimum distance volatility; η j =(1 / m)×∑ m j=1 (η) j -(1 / m)×∑ m j=1 η j )) 2 .

[0042] In this embodiment, the variance corresponding to each minimum distance list in LT is used to represent the dispersion of the minimum distance in each minimum distance list. It can be understood that the smaller the variance, the smaller the fluctuation of the minimum distance in the minimum distance list, and the larger the variance, the greater the fluctuation of the minimum distance in the minimum distance list.

[0043] S520, based on η, determine the target minimum distance volatility HU=∑ mj=1 η j .

[0044] S530, if HU < HU', then the data corresponding to the latest n flight trajectory points are determined to be false trajectory point data; otherwise, the data corresponding to the latest n flight trajectory points are determined not to be false trajectory point data; where HU' is the preset target minimum distance fluctuation rate threshold.

[0045] In this embodiment, if HU < HU', it means that the minimum distance in each minimum distance list in LT fluctuates less and shows a relatively regular increase or decrease, so that the data corresponding to the latest n flight trajectory points can be determined as false trajectory point data.

[0046] Furthermore, to improve the accuracy of the judgment, step S500 may include the following steps:

[0047] S511, iterate through η, if η j <η' j Then determine D j The normalized flight data within the range exhibits a linear variation; where η' j This is the minimum distance volatility threshold corresponding to the preset j-th type of flight data.

[0048] In this embodiment, each type of flight data corresponds to a minimum distance volatility threshold. This minimum distance volatility threshold can be obtained through the analysis of a large amount of historical data. For example, several sets of historical flight data of a certain type, including n flight trajectory points, are obtained. Then, the minimum distance volatility corresponding to each set is calculated, and the minimum distance volatility among the minimum distance volatility is determined as the minimum distance volatility threshold corresponding to that type of flight data.

[0049] S512, if the normalized flight data in each normalized flight data list in D are linearly related, then the data corresponding to the latest n flight trajectory points are determined to be false trajectory point data; otherwise, the data corresponding to the latest n flight trajectory points are determined not to be false trajectory point data.

[0050] In this embodiment, the data corresponding to the latest n flight trajectory points are determined to be false trajectory point data only when the normalized flight data in each normalized flight data list in D has a linear change relationship; thus, the accuracy of the judgment can be ensured.

[0051] The method for determining false flight trajectory point data in this embodiment constructs flight data groups from the normalized flight data corresponding to the acquired n flight trajectory points to obtain a flight data group list set EA corresponding to the normalized flight data list set. Each flight data group list in EA is fitted to obtain a linear regression function corresponding to each flight data group list. The minimum distance between each flight data group in each flight data group list and the corresponding linear regression function is obtained. If each minimum distance list set satisfies a preset second judgment condition, the data corresponding to the latest n flight trajectory points is determined to be false trajectory point data. This avoids false flight trajectory point data being used by the data monitoring platform, which could lead to data monitoring anomalies on the data monitoring platform.

[0052] In an exemplary embodiment, to improve computational efficiency, the method for determining false flight trajectory point data may further include the following steps:

[0053] Q100, obtain the latest normalized flight data for each type corresponding to the n flight trajectory points, to obtain a normalized flight data list set D = (D1, D2, ..., D...). j ,…,D m ), j=1,2,…,m; where, D j D represents the list of normalized flight data of type j, where m is the number of normalized flight data types; j =(D 1，j D 2，j D i，j D n，j ), i=1,2,…,n; D i，j This refers to the j-th type of normalized flight data corresponding to the i-th flight trajectory point.

[0054] In this embodiment, D can be obtained through the following steps:

[0055] Q110, obtain the latest flight data corresponding to the n flight trajectory points to obtain the initial flight data list set A = (A1, A2, ..., A...). i ,…,A n ); where A i A is the initial flight data list corresponding to the i-th flight trajectory point; i = (A i，1 A i，2 A i，j A i，m A i，j The i-th flight trajectory point is the j-th type of flight data in the initial flight data list, where m is the number of flight data types; the r-th flight trajectory point is adjacent to the (r+1)-th flight trajectory point; r = 1, 2, ..., n-1.

[0056] In this embodiment, the civil aviation sector uses Automatic Dependent Surveillance-Broadcast (ADS-B) to acquire flight data corresponding to aircraft flight trajectory points within a certain area in real time. It is understood that the acquired flight data corresponding to the aircraft flight trajectory points may be generated manually, i.e., false flight data. Each flight trajectory point corresponds to j types of flight data, such as flight altitude, flight speed, longitude, or latitude. One flight trajectory point corresponds to one flight data, thus obtaining an initial flight data list. The acquired n flight trajectory points correspond to consecutive flight trajectory points of the same aircraft.

[0057] Q120, Based on A, obtain the intermediate flight data list set B = (B1, B2, ..., B...) for each type of flight data. j ,…,B m ); where B j B is the intermediate flight data list corresponding to the j-th type of flight data. j = (A 1，j A 2，j A i，j A n，j ).

[0058] In this embodiment, each initial flight data list includes j types of flight data, which can acquire the same type of flight data for each flight trajectory point to obtain B; for example, the flight altitude corresponding to each flight trajectory point is put into the same intermediate flight data list to obtain the intermediate flight data list corresponding to the flight altitude.

[0059] Q130, normalize the flight data in each intermediate flight data list in B to obtain the normalized flight data list set D corresponding to B.

[0060] Furthermore, step Q130 may include the following steps:

[0061] Q131, obtain the preset maximum value (MAX) corresponding to the j-th data type. j and preset minimum value MIN j .

[0062] In this embodiment, MAX j and MIN j This can be obtained from several historical flight data of type j. It is understandable that MAX... j and MIN j It is fixed and unchanging.

[0063] Q132, according to MAX j and MIN j Determine D i，j = (A i，j -MIN j ) / (MAX j -MIN j ); thus we obtain D.

[0064] It should be noted that the normalization method in this embodiment is different from the normalization method in the above embodiments. In this embodiment, MAX j and MIN j It is fixed and unchanging, while B in the above embodiment is different. j，max and B j，min Each time the latest flight data corresponding to the n flight trajectory points is obtained, it may change; MAX j and MIN j Set it to a fixed value to facilitate subsequent calculations.

[0065] Q200, determine any normalized flight data list in D as the target flight data list, so as to obtain the target flight data list C.

[0066] In this embodiment, any normalized flight data list in D is determined as the target flight data list. Then, it is determined whether each other normalized flight data in C is collinear with the target flight data list. If so, it means that all intermediate flight data lists in D are collinear, and the flight data in each intermediate flight data list in B is determined to be linearly changing.

[0067] Q300, construct a corresponding determinant for each normalized intermediate flight data list in C and the target flight data list, to obtain a determinant list MD = (MD1, MD2, ..., MD300). p , ...,MD m-1 ), p=1,2,…,m-1; where MD p Let MD be the determinant of the p-th normalized intermediate flight data list and the target flight data list in C; p Includes target flight data line MD1 and normalized intermediate flight data line MD2 p ;MD1=(MD11,MD12,…,MD1 i , ..., MD1 n ); MD2 p =(MD2) p，1 MD2 p，2 , ..., MD2 p，i , ..., MD2 p，n ); among which, MD1 iMD2 represents the i-th flight data in the target flight data list in C; p，i This refers to the i-th flight data in the p-th normalized intermediate flight data list in C.

[0068] In this embodiment, MD p Given a determinant with two rows and n columns, it's understandable that the value of the determinant cannot be directly calculated; MD p The positional relationship of each data point in the first row of MD is the same as the positional relationship of the flight data in the target flight data list in C. Similarly, MD p The positional relationship of each data point in the second row of C is the same as the positional relationship of the flight data in the p-th normalized intermediate flight data list in C.

[0069] Q400, via MD p The corresponding preset historical sub-determinant value list LQ p Get MD p The corresponding subdeterminant values ​​for the first n-2 subdeterminants; where LQ p =(LQ p，1 LQ p，2 , ..., LQ p，a , ..., LQ p，n-2 ), a=1, 2,…, n-2; LQ p，a To be with MD p The subdeterminant value of the (a+1)th subdeterminant corresponding to the adjacent preceding row determinant.

[0070] In this embodiment, after step Q300 and before step Q400, the method includes the following steps:

[0071] Q310, determine whether the normalized flight data of each type corresponding to the latest n flight trajectory points is the first time it has been acquired;

[0072] Q320, if this is the first time obtaining the data, proceed to step S1, which involves calculating each sub-determinant corresponding to each row and storing the calculated sub-determinant value for each sub-row into D. p The corresponding preset historical sub-determinant value list LQ p This allows for direct access to the calculation results in the next calculation, eliminating the need for repeated calculations and thus improving computational efficiency.

[0073] Q330, if this is not the first time obtaining the data, proceed to step Q400.

[0074] Q1, MD p Decompose into n sub-determinants to obtain MD p The corresponding list of subdeterminants H p=(H p，1 H p，2 H P，i H p，n ); where H P，i For MD p The corresponding i-th sub-determinant; H p，q The first line includes MD1 q and MD1 q+1 H p，q The second line includes MD2 p，q and MD2 p，q+1 ; q = 1, 2, ..., n-1; H p，n The first line includes MD1 n And MD11, H p，n The second line includes MD2 p，n and MD2 p，1 .

[0075] In this embodiment, MD can be p Decompose it into n sub-determinants to obtain H. p .

[0076] Q2, Get MD p The corresponding subdeterminant value for each subdeterminant is used to obtain MD. p The corresponding list of subdeterminant values ​​Q p =(Q p，1 Q p，2 Q p，i Q p，n ); where Q p，q For H P，q The corresponding subdeterminant value; Q p，q =|MD1 q ×MD2 p，q+1 -MD1 q+1 ×MD2 p，q |;Q p，n =|MD1 n ×MD2 p，1 -MD11×MD2 p，n |

[0077] According to the method of calculating determinants, for MD p The determinant value corresponding to each sub-determinant is calculated to obtain Q. p .

[0078] Q500, according to MD p Determine MD p The corresponding subdeterminant value Q of the (n-1)th subdeterminant p，n-1 =|MD1 n-1 ×MD2p，n -MD1 n ×MD2 p，n-1 | and D p The corresponding subdeterminant value Q of the nth subdeterminant p，n =|MD1 n ×MD2 p，1 -MD11×MD2 p，n |

[0079] In this embodiment, it can be understood that after obtaining the latest n flight trajectory points corresponding to each type of normalized flight data, MD p Of the corresponding n sub-determinants, only the last two sub-determinants need to be recalculated, while the values ​​of the sub-determinants corresponding to the first n-2 sub-determinants can be obtained directly.

[0080] Q600, according to LQ p Q p，n-1 and Q p，n Determine D p The corresponding second sub-flight data linearity WD p =∑ n-2 a=1 Q p，a +Q p，n-1 +Q p，n ; and thus obtain the second sub-flight data linearity list WD = (WD1, WD2, ..., WD) corresponding to D. p , ...,WD m-1 ).

[0081] In this embodiment, for each type of normalized flight data corresponding to the latest n flight trajectory points that are not acquired for the first time, only two sub-determinants need to be calculated when performing determinant calculation on each type of data, thereby greatly improving the calculation efficiency.

[0082] Q700, LQ p LQ in p，b Replace with LQ p，b+1 Where b = 1, 2, ..., n-2.

[0083] In this embodiment, for D p After the corresponding sub-determinants have been calculated, LQ needs to be processed. p The sub-determinant values ​​stored in the system are updated so that when the latest flight data corresponding to the n flight trajectory points is obtained next time, the calculated sub-determinant values ​​can be directly obtained when calculating the determinant.

[0084] Q800: If the linearity of the flight data in WD meets the preset second judgment condition, then the latest n flight trajectory point data are determined to be collinear.

[0085] Furthermore, step Q800 may include the following steps:

[0086] Q810, Based on WD, determine the second flight data linearity WY corresponding to D = ∑ m-1 p=1 WD p ;

[0087] Q820, if WY < ε, then the latest n flight trajectory points are determined to be collinear; where ε is a preset threshold for the linearity of total flight data.

[0088] In this embodiment, it should be noted that, ideally, if the flight data in the p-th normalized intermediate flight data list in C and the flight data in the target flight data list both exhibit a standard linear relationship, then D p Corresponding sub-flight data linearity WD p =0; However, in reality, data calculations may contain errors. Even if the flight data in the p-th normalized intermediate flight data list in C and the flight data in the target flight data list have a linear relationship, WD p It may not equal 0, but WD p It is close to 0; therefore, in order to improve the generalization of the judgment, ε is set, thereby avoiding misjudgment and improving the accuracy of the judgment.

[0089] Furthermore, ε can be determined through the following steps:

[0090] Q821, Determine the range of flight trajectory points corresponding to n according to the preset total flight data linearity threshold mapping table; wherein, the preset total flight data linearity threshold mapping table includes several rows, each row corresponding to a preset range of flight trajectory points and the associated total flight data linearity threshold.

[0091] In this embodiment, through the analysis of a large amount of historical data, the total flight data linearity threshold corresponding to each type of flight data for different ranges of flight trajectory points can be determined, and then a preset total flight data linearity threshold mapping table is generated. It can be understood that for the same type of flight data, the more flight trajectory points there are, the larger the corresponding total flight data linearity threshold will be.

[0092] Q822, the linearity threshold of the total flight data associated with the number of flight trajectory points corresponding to n is determined as ε.

[0093] In this embodiment, n determines the number of sub-determinants corresponding to each determinant. Therefore, the more sub-determinants there are, the greater the linearity of the flight data for this type of flight data. Thus, the specific ε is determined based on the size of n, further improving the accuracy of the judgment.

[0094] Furthermore, the flight data includes the time, altitude, speed, longitude, and latitude corresponding to the flight trajectory points.

[0095] In this embodiment, normalized flight data of each type corresponding to the latest n flight trajectory points are obtained to obtain a normalized flight data list set D; any normalized flight data list in D is determined as the target flight data list to obtain the target flight data list C; each normalized intermediate flight data list in C is constructed with the target flight data list to form a corresponding determinant to obtain a determinant list MD; then, based on MD, it is determined whether the latest n flight trajectory point data are collinear; and then, the determinant MD in MD is... p When calculating the subdeterminant values ​​of the corresponding n subdeterminants, the n-2 subdeterminant values ​​can be directly obtained from MD. p The corresponding preset historical sub-determinant value list LQ p The calculation can be obtained by simply calculating the values ​​of two sub-determinants, which greatly improves the computational efficiency.

[0096] In one exemplary embodiment, the method for determining false flight path point data may further include the following steps:

[0097] T100, retrieve the latest flight data corresponding to the n flight trajectory points to obtain the initial flight data list set A = (A1, A2, ..., A...). i ,…,A n ); where A i A is the initial flight data list corresponding to the i-th flight trajectory point; i = (A i，1 A i，2 A i，j A i，m ), j=1,2,…,m; A i，j The i-th flight data is the j-th type of flight data in the initial flight data list corresponding to the i-th flight trajectory point, where m is the number of flight data types; the r-th flight trajectory point is adjacent to the (r+1)-th flight trajectory point and corresponds to the same aircraft; r = 1, 2, ..., n-1.

[0098] In this embodiment, the civil aviation sector uses Automatic Dependent Surveillance-Broadcast (ADS-B) to acquire flight data corresponding to aircraft flight trajectory points within a certain area in real time. It is understood that the acquired flight data corresponding to the aircraft flight trajectory points may be generated manually, i.e., false flight data. Each flight trajectory point corresponds to j types of flight data, such as flight altitude, flight speed, longitude, or latitude. One flight trajectory point corresponds to one flight data, thus obtaining an initial flight data list. The acquired n flight trajectory points correspond to consecutive flight trajectory points of the same aircraft.

[0099] T200, based on A, obtain the intermediate flight data list set B = (B1, B2, ..., B...) corresponding to each type of flight data. j ,…,B m ); where B j B is the intermediate flight data list corresponding to the j-th type of flight data. j = (A 1，j A 2，j A i，j A n，j ).

[0100] In this embodiment, each initial flight data list includes j types of flight data, which can acquire the same type of flight data for each flight trajectory point to obtain B; for example, the flight altitude corresponding to each flight trajectory point is put into the same intermediate flight data list to obtain the intermediate flight data list corresponding to the flight altitude.

[0101] T300 uses a preset linear change judgment method to determine whether the flight data in each intermediate flight data list in B changes linearly.

[0102] In this embodiment, for each flight data point in each intermediate flight data list, the data will change during normal flight. For example, the flight altitude may differ between adjacent flight trajectory points. Specifically, step T300 may include the following steps:

[0103] T310 uses a preset normalization method to normalize the flight data in each intermediate flight data list in B, so as to obtain the normalized flight data list set D = (D1, D2, ..., D...) corresponding to B. j ,…,D m ); where D j For B j The corresponding normalized flight data list; D j =(D1，j D 2，j D i，j D n，j ); D i，j For A i，j The corresponding normalized flight data.

[0104] In this embodiment, the flight data in each intermediate data list in B is first normalized so that each type of flight data is between 0 and 1, which facilitates subsequent processing.

[0105] Furthermore, D i，j = (A i，j -B j，min ) / (B j，max -B j，min ); B j，max For B j The largest flight data in China, B j，min For B j The smallest flight data.

[0106] Regarding B j You can get B j The largest and smallest flight data in the data, and then the A i，j Normalization is performed.

[0107] T320 determines any normalized flight data list in D as the target flight data list to obtain the target flight data list C.

[0108] T330, if each normalized intermediate flight data list in C and the target flight data list are collinear, then the flight data in each intermediate flight data list in B is determined to be linearly changing.

[0109] In this embodiment, any normalized flight data list in D is determined as the target flight data list. Then, it is determined whether each other normalized flight data in C is collinear with the target flight data list. If so, it means that all intermediate flight data lists in D are collinear, and the flight data in each intermediate flight data list in B is determined to be linearly changing.

[0110] Furthermore, step T330 may include the following steps:

[0111] T331, construct a corresponding determinant for each normalized intermediate flight data list in C and the target flight data list respectively, to obtain a determinant list MD = (MD1, MD2, ..., MD331). p , ...,MD m-1 ), p=1,2,…,m-1; where MD pLet MD be the determinant of the p-th normalized intermediate flight data list and the target flight data list in C; p Includes target flight data line MD1 and normalized intermediate flight data line MD2 p ;MD1=(MD11,MD12,…,MD1 i , ..., MD1 n ); MD2 p =(MD2) p，1 MD2 p，2 , ..., MD2 p，i , ..., MD2 p，n ); among which, MD1 i MD2 represents the i-th flight data in the target flight data list in C; p，i This refers to the i-th flight data in the p-th normalized intermediate flight data list in C.

[0112] In this embodiment, MD p Given a determinant with two rows and n columns, it's understandable that the value of the determinant cannot be directly calculated; MD p The positional relationship of each data point in the first row of MD is the same as the positional relationship of the flight data in the target flight data list in C. Similarly, MD p The positional relationship of each data point in the second row of C is the same as the positional relationship of the flight data in the p-th normalized intermediate flight data list in C.

[0113] T332, MD p Decompose into n sub-determinants to obtain MD p The corresponding list of subdeterminants H p =(H p，1 H p，2 H P，i H p，n ); where H P，i For MD p The corresponding i-th sub-determinant; H p，q The first line includes MD1 q and MD1 q+1 H p，q The second line includes MD2 p，q and MD2 p，q+1 ; q = 1, 2, ..., n-1; H p，n The first line includes MD1 n And MD11, H p，n The second line includes MD2 p，n and MD2 p，1 .

[0114] In this embodiment, MD can be pDecompose it into n sub-determinants to obtain H. p .

[0115] T333, Get MD p The corresponding subdeterminant value for each subdeterminant is used to obtain MD. p The corresponding list of subdeterminant values ​​Q p =(Q p，1 Q p，2 Q p，i Q p，n ); where Q p，q For H P，q The corresponding subdeterminant value; Q p，q =|MD1 q ×MD2 p，q+1 -MD1 q+1 ×MD2 p，q |;Q p，n =|MD1 n ×MD2 p，1 -MD11×MD2 p，n |

[0116] According to the method of calculating determinants, for MD p The determinant value corresponding to each sub-determinant is calculated to obtain Q. p .

[0117] T334, according to Q p Determine D p The corresponding first sub-flight data linearity XD p =∑ n i=1 Q p，i ; and thus obtain the first sub-flight data linearity list XD = (XD1, XD2, ..., XD) corresponding to D. p ... XD m-1 ).

[0118] Through the above steps, D can be obtained. p The corresponding first sub-flight data linearity XD p .

[0119] T335, if the linearity of the flight data in XD meets the preset first judgment condition, then the flight data in each intermediate flight data list in B is determined to be linearly changing.

[0120] Furthermore, step T335 may include the following steps:

[0121] T31, based on XD, determine the flight data linearity QY corresponding to D = ∑ m-1 p=1 XD p .

[0122] T32, if QY < ε, then the flight data in each intermediate flight data list in B is determined to be linearly changing; where ε is a preset threshold for the linearity of total flight data.

[0123] In this embodiment, it should be noted that, ideally, if the flight data in the p-th normalized intermediate flight data list in C and the flight data in the target flight data list both exhibit a standard linear relationship, then MD p Corresponding sub-flight data linearity XD p =0; However, in reality, data calculations may contain errors. Even if the flight data in the p-th normalized intermediate flight data list in C and the flight data in the target flight data list have a linear relationship, XD p It might not equal 0, but, XD p It is close to 0; therefore, in order to improve the generalization of the judgment, ε is set, thereby avoiding misjudgment and improving the accuracy of the judgment.

[0124] Furthermore, ε can be determined through the following steps:

[0125] T34 determines the range of flight trajectory points corresponding to n based on the preset total flight data linearity threshold mapping table; wherein, the preset total flight data linearity threshold mapping table includes several rows, each row corresponding to a preset range of flight trajectory points and the associated total flight data linearity threshold.

[0126] In this embodiment, through the analysis of a large amount of historical data, the total flight data linearity threshold corresponding to each type of flight data for different ranges of flight trajectory points can be determined, and then a preset total flight data linearity threshold mapping table is generated. It can be understood that for the same type of flight data, the more flight trajectory points there are, the larger the corresponding total flight data linearity threshold will be.

[0127] T35, the linearity threshold of the total flight data associated with the number of flight trajectory points corresponding to n is determined as ε.

[0128] In this embodiment, n determines the number of sub-determinants corresponding to each determinant. Therefore, the more sub-determinants there are, the greater the linearity of the flight data for this type of flight data. Thus, the specific ε is determined based on the size of n, further improving the accuracy of the judgment.

[0129] Furthermore, to improve the accuracy of the judgment, step T335 may include the following steps:

[0130] T33, if the linearity of each sub-flight data in XD is less than the corresponding preset sub-flight data linearity threshold, then the flight data in each intermediate flight data list in B is determined to be linearly changing.

[0131] In this embodiment, each type of flight data corresponds to a preset sub-flight data linearity threshold. Only when the linearity of each sub-flight data in XD is less than the corresponding preset sub-flight data linearity threshold is the flight data in each intermediate flight data list in B determined to be linearly changing. This method is more accurate than the method in the above embodiment.

[0132] T400: If the flight data in each intermediate flight data list in B changes linearly, then the flight data corresponding to the latest n flight trajectory points is determined to be false flight data; otherwise, the flight data corresponding to the latest n flight trajectory points is determined not to be false flight data.

[0133] In this embodiment, if the flight data in each intermediate flight data list in B changes linearly, it means that the latest n flight trajectory points correspond to each type of flight data according to the same data change pattern, such as increasing or decreasing; such data change pattern is likely to be flight data generated by artificially set rules, i.e., false flight data.

[0134] In this embodiment, an initial flight data list corresponding to the latest flight trajectory points for the same aircraft is obtained. Then, the flight data of the same type corresponding to each flight trajectory point is placed into the same intermediate flight data list. A preset linear change judgment method is used to determine whether the flight data in each intermediate flight data list are all linearly changing. If they are all linearly changing, it means that the flight data in the initial flight data list corresponding to the latest flight trajectory points is generated manually and is false flight data; otherwise, the flight data in the initial flight data list corresponding to the latest flight trajectory points is not false flight data. This accurately identifies false flight data and prevents false flight data from being used by the data monitoring platform, which would greatly reduce the accuracy of the aircraft data monitored by the data monitoring platform.

[0135] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0136] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0137] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0138] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0139] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0140] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0141] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0142] The electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0143] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0144] The memory stores program code that can be executed by the processor, causing the processor to perform the steps in the various embodiments described in this specification.

[0145] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0146] The memory may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0147] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus structures.

[0148] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0149] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0150] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0151] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A method for determining false flight trajectory point data, characterized in that, The method comprises the following steps: S100, obtaining a latest normalized flight data list set D corresponding to n flight trajectory points; j D is a normalized flight data list of the jth type, j is 1 to m, and m is the number of normalized flight data types; i，j D is the jth type of normalized flight data corresponding to the ith flight trajectory point, i is 1 to n; the flight data includes time, flight height, flight speed, longitude and latitude corresponding to the flight trajectory point; S200, determining, according to D, a flight data group corresponding to each type of normalized flight data of each trajectory point to obtain a flight data group list set EA corresponding to D; EA j for D j corresponding to D; EA i，j for the jth type of normalized flight data of the ith flight trajectory point; EA i，j = (i, D i，j ); S300, using a preset linear regression fitting method, performing linear fitting on the EA j to obtain a linear regression function y(i)=u×i+v; u is a weight of the linear regression function obtained by performing linear fitting on the EA j , and v is a bias of the linear regression function obtained by performing linear fitting on the EA j . j ​ S400, obtaining EA j The minimum distance between each coordinate point corresponding to the flight data set and y(i) is obtained to obtain EA j The minimum distance list LT corresponding to EA j The minimum distance list set LT corresponding to EA is obtained; LT i，j is EA i，j The minimum distance between each coordinate point corresponding to the flight data set and y(i) is obtained to obtain EA S500, if each minimum distance list set in the LT satisfies a preset second judgment condition, determining that the data corresponding to the latest n flight trajectory points is false trajectory point data; Step S500 comprises the following steps: S510, determining a minimum distance fluctuation rate corresponding to each minimum distance list in the LT according to the LT, to obtain a minimum distance fluctuation rate list η corresponding to the LT; η j for the LT j corresponding minimum distance fluctuation rate; η j = (1 / n) x ∑ n i=1 (LT i，j - ((1 / n) x ∑ n i=1 LT i，j )) 2 ; S520, according to η, determine the target minimum distance fluctuation rate HU corresponding to LT =∑ m j=1 η j ; S530, if HU S530, if HU S110, obtain an initial flight data list set A corresponding to the latest n flight trajectory points; A i is the initial flight data list corresponding to the i-th flight trajectory point; A i，j is the j-th type of flight data in the initial flight data list corresponding to the i-th flight trajectory point; the r-th flight trajectory point is adjacent to the r+1-th flight trajectory point; r takes 1 to n-1; S120, according to A, obtaining a set of intermediate flight data lists B corresponding to each type of flight data; B j is the set of intermediate flight data lists corresponding to the jth type of flight data. D is obtained by the following steps:

2. The false flight trajectory point data determination method of claim 1, wherein, S130, performing normalization processing on the flight data in each intermediate flight data list in B to obtain D. S131, acquire the preset maximum value MAX corresponding to the jth type of data j and the preset minimum value MIN j ; S132, according to MAX j and MIN j Determine D i，j = (A i，j -MIN j ) / (MAX j -MIN j ); and thus we get D.

3. A non-transitory computer-readable storage medium having stored therein at least one instruction or at least one piece of program, characterized in that, Step S130 comprises the following steps:

4. An electronic device, comprising: The at least one instruction or the at least one program is loaded and executed by the processor to implement the false flight trajectory point data determination method of claim 1 or 2. The non-transitory computer readable storage medium of claim 3 comprises a processor. The non-transitory computer readable storage medium of claim 3 comprises a processor.

Images