Method for determining co-linearity of flight trajectory point data, electronic device and storage medium

Abstract

The application provides a flight trajectory point data collinearity determination method, electronic equipment and a storage medium, relates to the technical field of flight trajectory point data collinearity determination, and the method comprises the following steps: obtaining a normalized flight data list set D; obtaining target flight data list C; obtaining a determinant list MD; obtaining MD p The corresponding first n-2 sub-determinants correspond to sub-determinant values; MD p The corresponding first n-1 sub-determinant corresponds to a sub-determinant value Q p，n‑1 And D p The corresponding first n sub-determinant corresponds to a sub-determinant value Q p，n ; D p The corresponding second flight data linearity WD p ; LQ p In LQ p，b Is replaced by LQ p，b+1 ; if the flight data linearity in WD meets a preset second judgment condition, the latest n flight trajectory point data collinearity is determined; the application can greatly improve the calculation efficiency, and further improve the efficiency of flight trajectory point data collinearity determination.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flight trajectory point data collinearity determination, and particularly relates to a flight trajectory point data collinearity determination method, an electronic device and a storage medium. BACKGROUND

[0002] In the field of civil aviation, the data monitoring platform of an airport usually uses an ADS-B system to obtain real-time flight data of an aircraft, so as to realize real-time monitoring of flight information of each aircraft. However, the flight data of the aircraft received by the ADS-B system is not necessarily completely real flight data corresponding to the aircraft, and there may be false data maliciously implanted. For example, false flight data automatically generated by software in a human manner. In general, when identifying false data, it is necessary to calculate whether the data of multiple consecutive trajectory points is linearly changed. If each newly added flight trajectory point is calculated with the data of the latest multiple flight trajectory points, the calculation amount of data will be large, and the efficiency of flight trajectory point data collinearity will be low. SUMMARY

[0003] In view of the above technical problems, the technical scheme adopted by the present application is as follows:

[0004] According to a first aspect of the present application, a flight trajectory point data collinearity determination method is provided, the method comprising the following steps:

[0005] S100, obtaining each type of normalized flight data corresponding to the latest n flight trajectory points to obtain a normalized flight data list set D=(D1, D2,..., Dm), j=1, 2,..., m; wherein Djis the jth type of normalized flight data list, and m is the number of normalized flight data types; D=(D1, D2,..., Dn), i=1, 2,..., n; Djis the jth type of normalized flight data corresponding to the ith flight trajectory point. j ,…,D m ) j j ,…,D 1，j ,…,D 2，j ,…,D i，j ,…,D n，j ,…,D i，j

[0006] S200, determining any normalized flight data list in D as a target flight data list to obtain a target flight data list C.

[0007] S300, constructing each normalized intermediate flight data list in C and the target flight data list into corresponding determinants to obtain a determinant list MD=(MD1, MD2,..., MDm), i=1, 2,..., n; MDjis the jth type of determinant corresponding to the ith normalized intermediate flight data list. p ,…,MD​​m-1 ), p = 1, 2, …, m-1; wherein MD p is the determinant corresponding to the pth normalized intermediate flight data list and the target flight data list in C; MD p includes the target flight data row MD1 and the normalized intermediate flight data row MD2 p ; MD1 = (MD11, MD12, …, MD1 i , …, MD1 n ); MD2 p = (MD2 p，1 , MD2 p，2 , …, MD2 p，i , …, MD2 p，n ); wherein MD1 i is the ith flight data in the target flight data list in C; MD2 p，i is the ith flight data in the pth normalized intermediate flight data list in C.

[0008] S400, obtains the corresponding preset historical sub-determinant value list LQ p corresponding to MD p , and obtains the sub-determinant values corresponding to the first n-2 sub-determinants corresponding to MD p ; wherein LQ p = (LQ p，1 , LQ p，2 , …, LQ p，a , …, LQ p，n-2 ), a = 1, 2, …, n-2; LQ p，a is the sub-determinant value of the a+1th sub-determinant corresponding to the previous determinant adjacent to MD p .

[0009] S500, according to MD p , determines the sub-determinant value Q p corresponding to the n-1th sub-determinant corresponding to MD p，n-1 = |MD1 n-1 ×MD2 p，n -MD1 n ×MD2 p，n-1 |, and the sub-determinant value Q p，n corresponding to the nth sub-determinant corresponding to D p = |MD1 n ×MD2 p，1 -MD11×MD2 p，n |.

[0010] S600, according to LQ p , Q p，n-1 and Q p，n , determines Dp Corresponding second sub-flight data linearity WD p =∑ n-2 a＝1 Q p，a +Q p，n-1 +Q p，n ; and further obtain a second sub-flight data linearity list WD corresponding to D, WD=(WD1, WD2, …, WD p , …, WD m-1 ).

[0011] S700, replace LQ p in LQ p，b with LQ p，b+1 ; wherein b=1, 2, …, n-2.

[0012] S800, if the flight data linearity in WD meets the preset second judgment condition, it is determined that the latest n flight trajectory point data are collinear.

[0013] According to another aspect of the application, a non-transitory computer readable storage medium is also provided, the storage medium storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by a processor to implement the flight trajectory point data collinearity determination method described above.

[0014] According to another aspect of the application, an electronic device is also provided, comprising a processor and the non-transitory computer readable storage medium described above.

[0015] The application has at least the following beneficial effects:

[0016] The flight trajectory point data collinearity determination method of the application obtains normalized flight data of each type corresponding to the latest n flight trajectory points to obtain a normalized flight data list set D; determines any normalized flight data list in D as a target flight data list to obtain a target flight data list C; constructs each normalized intermediate flight data list in C and the target flight data list into corresponding determinants to obtain a determinant list MD; then judges whether the latest n flight trajectory point data are collinear according to MD; when calculating the determinant values of the n sub-determinants corresponding to MD p , the n-2 determinant values can be directly obtained from the preset historical determinant value list LQ p corresponding to MD p , and only two determinant values need to be calculated, thereby greatly improving the calculation efficiency and further improving the efficiency of flight trajectory point data collinearity judgment. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on these drawings.

[0018] Figure 1 The flowchart of the flight trajectory point data collinearity determination method provided by the embodiments of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of the present application.

[0020] It should be noted that based on the present disclosure, those skilled in the art should 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 the aspects described herein can be used to implement an apparatus and / or practice a method. In addition, this apparatus and / or practice this method can be implemented using other structures and / or functionality in addition to one or more of the aspects described herein.

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of the present application. Figure 1 The flowchart of the flight trajectory point data collinearity determination method provided by the embodiments of the present application.

[0022] The flight trajectory point data collinearity determination method can include the following steps:

[0023] S100, obtaining each type of normalized flight data corresponding to the latest n flight trajectory points to obtain a normalized flight data list set D=(D1, D2, …, Dm), j=1, 2, …, m; wherein Djis the jth type of normalized flight data list, and m is the number of normalized flight data types; D=(D1, D2, …, Dm), i=1, 2, …, n; Djis the jth type of normalized flight data corresponding to the ith flight trajectory point. j ,…,D m ), j=1, 2, …, m; wherein Djis the jth type of normalized flight data list, and m is the number of normalized flight data types; D=(D1, D2, …, Dm), i=1, 2, …, n; Djis the jth type of normalized flight data corresponding to the ith flight trajectory point. j j 1，j 2，j i，j n，j i，j ​​​​​​​

[0024] In this embodiment, D can be obtained by the following steps:

[0025] S110, obtaining flight data corresponding to the latest n flight trajectory points to obtain an initial flight data list set A=(A1, A2, …, An); i ,…,An-1 n ); Wherein, A i is the initial flight data list corresponding to the i-th flight trajectory point; A i =(A i，1 ,A i，2 ,…,A i，j ,…,A i，m ); 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, and 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.

[0026] In this embodiment, the civil aviation field obtains the flight data corresponding to the flight trajectory points of the aircraft in a certain area in real time through Automatic Dependent Surveillance-Broadcast (ADS-B). It can be understood that the flight data corresponding to the flight trajectory points of the aircraft obtained may be generated by artificial means, i.e. false flight data. Each flight trajectory point corresponds to j types of flight data, for example: flight data can be flight altitude, flight speed, longitude or latitude; One flight data corresponds to one flight trajectory point, so that an initial flight data list can be obtained; The n flight trajectory points obtained are continuous flight trajectory points corresponding to the same aircraft.

[0027] S120, according to A, obtaining an intermediate flight data list set B=(B1, B2, …, B j ,…,B m ) corresponding to each type of flight data; Wherein, B j is the j-th type of flight data corresponding to the intermediate flight data list, B j =(A 1，j ,A 2，j ,…,A i，j ,…,A n，j ).

[0028] In this embodiment, each initial flight data list includes j types of flight data, which can obtain the same type of flight data of 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.

[0029] 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.

[0030] Furthermore, step S130 may include the following steps:

[0031] S131, Obtain the preset maximum value MAX corresponding to the j-th data type. j and preset minimum value MIN j .

[0032] 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.

[0033] S132, according to MAX j and MIN j Determine D i，j =(A i，j -MIN j ) / MAX j -MIN j Therefore, we get D.

[0034] 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.

[0035] S200, determine any normalized flight data list in D as the target flight data list to obtain the target flight data list C.

[0036] 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.

[0037] S300, 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, ..., MD4). 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 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.

[0038] 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.

[0039] S400, 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 determinant.

[0040] In this embodiment, after step S300 and before step S400, the method includes the following steps:

[0041] S310, determining whether the current acquired latest n flight trajectory points correspond to each type of normalized flight data is the first acquisition;

[0042] S320, if it is the first acquisition, entering step S1, that is, calculating each sub-determinant corresponding to each determinant, and storing the calculated sub-determinant value corresponding to each sub-determinant to the D p corresponding to the preset historical sub-determinant value list LQ p , so as to directly obtain the calculation result next time without repeated calculation, thereby improving the calculation efficiency.

[0043] S330, if it is not the first acquisition, entering step S400.

[0044] S1, splitting MD p into n sub-determinants to obtain MD p corresponding to the sub-determinant list H p = (H p，1 , H p，2 , …, H P，i , …, H p，n ); wherein H P，i is the i-th sub-determinant corresponding to MD p ; the first row of H p，q includes MD1 q and MD1 q+1 , the second row of H p，q includes MD2 p，q and MD2 p，q+1 ; q = 1, 2, …, n-1; the first row of H p，n includes MD1 n and MD11, the second row of H p，n includes MD2 p，n and MD2 p，1 .

[0045] In this embodiment, MD p can be split into n sub-determinants, thereby obtaining H p .

[0046] S2, acquiring the sub-determinant value corresponding to each sub-determinant corresponding to MD p , to obtain the sub-determinant value list Q p corresponding to MD p = (Q p，1 , Q p，2 , …, Q p，i , …, Q p，n ); wherein Q p，q is H P，qThe 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 |

[0047] According to the method of calculating determinants, for MD p The determinant value corresponding to each sub-determinant is calculated to obtain Q. p .

[0048] S500, according to MD p Determine MD p The corresponding subdeterminant value Q of the (n-1)th subdeterminant p，n-1 =|MD1 n-1 ×MD2 p，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 |

[0049] 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.

[0050] S600, 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 ).

[0051] In the embodiment, for each type of normalized flight data corresponding to the latest n flight trajectory points acquired other than the first time, only two sub-determinants need to be calculated when calculating the determinant of each type of data, thereby greatly improving the calculation efficiency.

[0052] S700, replacing LQ p in LQ p，b with LQ p，b+1 ; wherein b = 1, 2,..., n-2.

[0053] In the embodiment, after the calculation of the sub-determinant corresponding to D p is completed, the sub-determinant value stored in LQ p needs to be updated, so that when the flight data corresponding to the latest n flight trajectory points is acquired next time, the calculated sub-determinant value can be directly obtained when calculating the determinant.

[0054] S800, if the linearity of the flight data in WD meets the preset second judgment condition, it is determined that the latest n flight trajectory point data is collinear.

[0055] Further, step S800 can include the following steps:

[0056] S810, according to WD, determining the second flight data linearity WY = ∑ m-1 p＝1 of D corresponding to WD p ;

[0057] S820, if WY < ε, it is determined that the latest n flight trajectory point data is collinear; wherein ε is a preset total flight data linearity threshold.

[0058] In the embodiment, it needs to be noted that, in an ideal case, if the flight data in the pth normalized intermediate flight data list in C and the flight data in the target flight data list are both in a standard linear change relationship, then the sub-flight data linearity WD p corresponding to D p = 0; however, in actual cases, there may be errors in data calculation, that is, even if the flight data in the pth normalized intermediate flight data list in C and the flight data in the target flight data list are in a linear change relationship, WD p may not be equal to 0, but WD p is close to 0; therefore, in order to have better generalization of the judgment, ε is set, thereby avoiding misjudgment and improving the accuracy of the judgment.

[0059] Further, ε can be determined by the following steps:

[0060] S821, 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.

[0061] 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.

[0062] S822, the linearity threshold of the total flight data associated with the number of flight trajectory points corresponding to n is determined as ε.

[0063] 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.

[0064] Furthermore, flight data includes the time, altitude, speed, longitude, and latitude corresponding to the flight trajectory points.

[0065] The method for determining collinearity of flight trajectory point data in this embodiment involves acquiring normalized flight data of each type corresponding to the latest n flight trajectory points to obtain a normalized flight data list set D; determining any normalized flight data list in D as the target flight data list to obtain the target flight data list C; constructing a corresponding determinant by combining each normalized intermediate flight data list in C with the target flight data list to obtain a determinant list MD; then determining whether the latest n flight trajectory point data are collinear based on MD; and finally, analyzing the determinant MD in MD... 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.

[0066] In an exemplary embodiment, based on the flight trajectory point collinearity determination method provided in the above embodiments, it is possible to further identify false flight data. Specifically, this may include the following steps:

[0067] 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 ); wherein, A i is an initial flight data list corresponding to the i-th flight trajectory point; A i = (A i，1 , A i，2 , …, A i，j , …, A i，m ), j = 1, 2, …, m; 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, and 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.

[0068] In this embodiment, the civil aviation field obtains the flight data corresponding to the flight trajectory points of the aircraft in a certain area in real time through Automatic Dependent Surveillance-Broadcast (ADS-B). It can be understood that the flight data corresponding to the flight trajectory points of the aircraft obtained may be generated by artificial means, i.e., false flight data. Each flight trajectory point corresponds to j types of flight data, for example: flight data can be flight altitude, flight speed, longitude or latitude; one flight trajectory point corresponds to one flight data, so that an initial flight data list can be obtained; the n flight trajectory points obtained are consecutive flight trajectory points corresponding to the same aircraft.

[0069] T200, according to A, obtaining a set of intermediate flight data lists B = (B1, B2, …, B j , …, B m ) corresponding to each type of flight data; wherein, B j is an intermediate flight data list corresponding to the j-th type of flight data, B j = (A 1，j , A 2，j , …, A i，j , …, A n，j ).

[0070] In this embodiment, each initial flight data list includes j types of flight data, which can obtain the same type of flight data of 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.

[0071] T300, using a preset linear change judgment method to judge whether the flight data in each intermediate flight data list in B is linearly changed.

[0072] 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:

[0073] 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 =(D 1，j D 2，j D i，j D n，j );D i，j For A i，j The corresponding normalized flight data.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] T320 determines any normalized flight data list in D as the target flight data list to obtain the target flight data list C.

[0078] 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.

[0079] 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.

[0080] Furthermore, step T330 may include the following steps:

[0081] 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 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 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.

[0082] 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.

[0083] 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，iH 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 .

[0084] In this embodiment, MD can be p Decompose it into n sub-determinants to obtain H. p .

[0085] 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 |

[0086] According to the method of calculating determinants, for MD p The determinant value corresponding to each sub-determinant is calculated to obtain Q. p .

[0087] 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 ).

[0088] By the above steps, D p corresponding first sub-flight data linearity XD p .

[0089] T335, if the flight data linearity in XD meets the preset first judgment condition, determine that the flight data in each intermediate flight data list in B is linearly changed.

[0090] Further, step T335 can include the following steps:

[0091] T31, according to XD, determine the flight data linearity QY = ∑ m-1 p＝1 XD p .

[0092] T32, if QY < ε, determine that the flight data in each intermediate flight data list in B is linearly changed; wherein ε is a preset total flight data linearity threshold.

[0093] In this embodiment, it needs to be noted that, in an ideal case, if the flight data in the pth normalized intermediate flight data list in C and the flight data in the target flight data list are both standard linearly changed, then MD p corresponding sub-flight data linearity XD p = 0; however, in actual case, there can be errors in data calculation, even if the flight data in the pth normalized intermediate flight data list in C and the flight data in the target flight data list are both linearly changed, XD p may not equal to 0, but XD p is close to 0; thus, in order to make the judgment more generalized, ε is set, thereby avoiding misjudgment and improving the accuracy of the judgment.

[0094] Further, ε can be determined by the following steps:

[0095] T34, according to the preset total flight data linearity threshold mapping table, determine the flight trajectory point quantity interval corresponding to n; wherein the preset total flight data linearity threshold mapping table includes several rows, and each row corresponds to a preset flight trajectory point quantity interval and an associated total flight data linearity threshold.

[0096] In this embodiment, through analysis of a large amount of historical data, the total flight data linearity threshold corresponding to each type of flight data in different number 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 the number of flight trajectory points, the greater the corresponding total flight data linearity threshold.

[0097] T35, the total flight data linearity threshold corresponding to the n pair of flight trajectory point number intervals is determined as ε.

[0098] In this embodiment, n determines the number of sub-determinants corresponding to each determinant, so the more sub-determinants, the greater the flight data linearity of this type of flight data. Therefore, the specific ε is determined according to the size of n, further improving the accuracy of the judgment.

[0099] Further, in order to improve the accuracy of the judgment, step T335 can include the following steps:

[0100] T33, if each sub-flight data linearity 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 changed.

[0101] In this embodiment, each type of flight data corresponds to a preset sub-flight data linearity threshold, and only when each sub-flight data linearity in XD is less than the corresponding preset sub-flight data linearity threshold, the flight data in each intermediate flight data list in B is determined to be linearly changed. Compared with the method in the above embodiment, this method is more accurate in judgment.

[0102] T400, if the flight data in each intermediate flight data list in B is linearly changed, it is determined that the flight data corresponding to the latest n flight trajectory points is false flight data; otherwise, it is determined that the flight data corresponding to the latest n flight trajectory points is not false flight data.

[0103] In this embodiment, if the flight data in each intermediate flight data list in B is linearly changed, it means that each type of flight data corresponding to the latest n flight trajectory points changes according to the same data change rule, for example, increases or decreases. Such data change rule is most likely to be generated by artificially set rules, that is, false flight data.

[0104] 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.

[0105] In an exemplary embodiment, based on the above embodiments, further determining false flight trajectory point data may specifically include the following steps:

[0106] 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.

[0107] In this embodiment, D can be obtained by the method in steps S100-S300 of the above embodiment, which will not be elaborated here.

[0108] Q200, based on D, determine the flight data set corresponding to each type of normalized flight data for each trajectory point, so as to obtain the flight data set list EA = (EA1, EA2, ..., EA2) 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，jThe flight data set corresponding to the normalized flight data of the jth type of the ith flight trajectory point; EA i，j = (i, D i，j ).

[0109] In this embodiment, the flight data set can be understood as the constructed X-axis coordinate and Y-axis coordinate, for example: EA i，j = (i, D i，j ), i is the X-axis coordinate, and D i，j is the Y-axis coordinate; so that each flight data set list can fit the corresponding regression straight line; it should be noted that in this embodiment, the time interval of the generation time of the flight data corresponding to the adjacent two flight trajectories is fixed, for example: the time interval is 50ms.

[0110] Q300, using a preset linear regression fitting method, linearly fitting EA j to obtain the linear regression function y(i) = u × i + v corresponding to EA j ; wherein, u is the weight of the linear regression function obtained by linearly fitting EA j , and v is the bias of the linear regression function obtained by linearly fitting EA j .

[0111] In this embodiment, the preset linear regression fitting method can be least square method; it should be noted that those skilled in the art can linearly fit EA j using the existing least square method according to actual needs to obtain the linear regression function y(i) = u × i + v corresponding to EA j , which is not described here.

[0112] Q400, obtaining the minimum distance of each coordinate point corresponding to the flight data set in EA j and y(i) to obtain the minimum distance list LT j = (LT j , LT 1，j , …, LT 2，j , …, LT i，j ) corresponding to EA n，j ; and obtaining the minimum distance list set LT = (LT1, LT2, …, LT j , …, LT m ) corresponding to EA i，j ; wherein, LT i，j is the minimum distance of EA j and y(i).

[0113] In this embodiment, it should be noted that those skilled in the art can use the existing point-to-line minimum distance calculation method to calculate EA jThe minimum distance between the coordinate point corresponding to each flight data set and y(i) is not elaborated here.

[0114] Q500, 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.

[0115] Furthermore, step Q500 may include the following steps:

[0116] Q510, 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, ..., η3) 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 .

[0117] 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.

[0118] Q520, Based on η, determine the target minimum distance volatility HU corresponding to LT = ∑ m j＝1 η j .

[0119] Q530, if HU < HU', then the data corresponding to the latest n flight trajectory points is determined to be false trajectory point data; otherwise, the data corresponding to the latest n flight trajectory points is determined not to be false trajectory point data; where HU' is the preset target minimum volatility threshold.

[0120] 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.

[0121] Furthermore, to improve the accuracy of the judgment, step Q500 may include the following steps:

[0122] Q511, iterate through η, if ηj η' j then it is determined that the normalized flight data in D j is a linear change relationship; wherein η' j is a preset minimum distance fluctuation rate threshold corresponding to the jth type of flight data.

[0123] In this embodiment, each type of flight data corresponds to a minimum distance fluctuation rate threshold. The minimum distance fluctuation rate threshold can be obtained by analyzing a large amount of historical data. For example, a plurality of groups of historical flight data of a certain type corresponding to n flight trajectory points are obtained, and then the minimum distance fluctuation rate corresponding to each group is calculated. The minimum distance fluctuation rate among the minimum distance fluctuation rates is determined as the minimum distance fluctuation rate threshold corresponding to the type of flight data.

[0124] Q512, if the normalized flight data in each normalized flight data list in D is a linear change relationship, it is determined that the data corresponding to the latest n flight trajectory points is false trajectory point data; otherwise, it is determined that the data corresponding to the latest n flight trajectory points is not false trajectory point data.

[0125] In this embodiment, only in the case that the normalized flight data in each normalized flight data list in D is a linear change relationship, it is determined that the data corresponding to the latest n flight trajectory points is false trajectory point data. Thus, the accuracy of the judgment can be ensured.

[0126] In this embodiment, the normalized flight data corresponding to the n flight trajectory points obtained is used to construct a flight data group to obtain a flight data group list set EA corresponding to a 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, it is determined that the data corresponding to the latest n flight trajectory points is false trajectory point data. Thus, the problem that false flight trajectory point data is used by the data monitoring platform and causes the data monitoring platform to have data monitoring abnormalities can be avoided.

[0127] In addition, although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps can be omitted, a plurality of steps can be combined into one step, and / or one step can be divided into a plurality of steps, etc.

[0128] The embodiment of the present application further provides a non-transitory computer readable storage medium, which can be arranged in an electronic device to save at least one instruction or at least one program related to a method in the method embodiment, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided by the above embodiment.

[0129] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0130] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which readable program codes are borne. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in connection with an instruction execution system, device or apparatus.

[0131] The program codes contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0132] The program codes for performing the operations of the present application can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, etc., and a conventional procedural programming language such as "C" language or similar programming language. The program codes can be completely executed on a user computing device, partially executed on a user device, executed as an independent software package, partially executed on a user computing device and partially executed on a remote computing device, or completely executed on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected through the Internet by using an Internet service provider).

[0133] Embodiments of the present application also provide an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0134] The electronic device is merely an example, and should not bring any limitation to the function and use range of embodiments of the present application.

[0135] The electronic device is in the form of a general computing device. The components of the electronic device can include, but are not limited to, the aforementioned at least one processor, the aforementioned at least one memory, a bus connecting different system components including the memory and the processor.

[0136] The memory stores program codes which can be executed by the processor, so that the processor executes steps in various embodiments described in the specification.

[0137] The memory can include a readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory, and can further include a read-only memory (ROM).

[0138] The memory can also include programs / utilities with a set of (at least one) program modules, such as an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which can include implementation of a network environment.

[0139] The bus can be 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 using any of a variety of bus structures.

[0140] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.) and can also communicate with one or more devices that enable a user to interact with the electronic device (and / or one or more input / output (I / O) devices 620), and / or with any devices (such as a router, a modem, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication can occur via an I / O interface. Also, the electronic device can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or the public network, such as the Internet) via a network adapter. The network adapter communicates with the other modules of the electronic device via the bus. It should be appreciated that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0141] Those skilled in the art can clearly understand the example embodiments described herein through the above description of the example embodiments, and the example embodiments described herein can be implemented by software or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.

[0142] Embodiments of the present disclosure also provide a computer program product comprising program code for causing an electronic device to perform the steps of the methods according to the various example embodiments of the present disclosure described above in the specification when the program product is run on the electronic device.

[0143] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for illustration, and are not intended to limit the scope of the present disclosure. 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 present disclosure.

Claims

1. A method for determining collinearity of flight trajectory point data, characterized in that, The method includes the following steps: S100, Obtain the latest normalized flight data list set D = (D1, D2, ..., Dn) corresponding to the n flight trajectory points. j ,…,D m ), j=1,2,…,m; 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 For the j-th type of normalized flight data corresponding to the i-th flight trajectory point; S200, determine any normalized flight data list in D as the target flight data list to obtain the target flight data list C; S300, 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, ..., MD4). p , ...,MD m-1 ), p=1, 2,…, m-1; 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 ); 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; S400, 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; 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 determinant; S500, according to MD p Determine MD p The corresponding subdeterminant value Q of the (n-1)th subdeterminant p，n-1 =|MD1 n-1 ×MD2 p，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 |; S600, 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+Qp，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 ); S700, LQ p LQ in p，b Replace with LQ p，b+1 Where b = 1, 2, ..., n-2; S800: 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.

2. The method for determining the collinearity of flight trajectory point data according to claim 1, characterized in that, D is obtained through the following steps: 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 ); Among them, 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 corresponds to 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; 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 ); 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.

3. The method for determining the collinearity of flight trajectory point data according to claim 2, characterized in that, Step S130 includes the following steps: S131, Obtain the preset maximum value MAX corresponding to the j-th data type. j and 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 Therefore, we get D.

4. The method for determining collinearity of flight trajectory point data according to claim 1, characterized in that, Step S800 includes the following steps: S810, based on WD, determine the second flight data linearity WY corresponding to D = ∑ m-1 p=1 WD p ; S820, if WY < ε, then the latest n flight trajectory point data are determined to be collinear; where ε is a preset total flight data linearity threshold.

5. The method for determining the collinearity of flight trajectory point data according to claim 1, characterized in that, Step S800 includes the following steps: S830, if the linearity of each second sub-flight data in WD is less than the corresponding preset sub-flight data linearity threshold, then the latest n flight trajectory point data are determined to be collinear.

6. The method for determining the collinearity of flight trajectory point data according to claim 4, characterized in that, ε is determined through the following steps: S821, 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. S822, the linearity threshold of the total flight data associated with the number of flight trajectory points corresponding to n is determined as ε.

7. The method for determining the collinearity of flight trajectory point data according to claim 2, characterized in that, Flight data includes the time, altitude, speed, longitude, and latitude corresponding to the flight trajectory points.

8. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the method for determining the collinearity of flight trajectory point data as described in any one of claims 1-7.

9. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 8.

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