Flight track segmentation method and device, medium and equipment

Abstract

The invention provides a flight path segmentation method and device, a medium and equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining ADS-B data corresponding to a to-be-processed path, so as to obtain a corresponding data list S; determining a target track point on the to-be-processed track according to the S; obtaining two target segmentation points according to a dichotomy, the target track point and the to-be-processed track; and segmenting the flight path according to the two segmentation points to obtain a climbing stage, a cruising stage and a descending stage. The automation degree is high, manpower is saved, and the segmentation result is more accurate.

Description

Background Art

[0002] In the aviation field, accurate analysis of aircraft flight trajectories is of vital importance to flight safety, aviation efficiency improvement, and air traffic management. Accurately dividing the various stages of an aircraft's flight trajectory can provide pilots with more accurate flight status information, helping them make more reasonable flight decisions, and can also provide air traffic control departments with more effective monitoring and scheduling basis.

[0003] At present, the existing methods for dividing aircraft flight trajectories mainly rely on simple threshold judgments of flight parameters and manual analysis based on experience. However, these methods have obvious limitations. On the one hand, simple threshold judgments are difficult to adapt to complex and changeable flight environments and diverse aircraft models. The parameter ranges of different aircraft in similar flight phases may vary greatly, resulting in inaccurate division; on the other hand, manual analysis is not only inefficient, but also easily affected by subjective factors, making it difficult to ensure the consistency and accuracy of the division results. Therefore, how to accurately divide the various stages of an aircraft's flight trajectory has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In response to the above technical problems, the present application provides a flight trajectory segmentation method, device, medium and equipment, which at least partially solve the problems existing in the prior art.

[0005] In a first aspect of the present application, a flight trajectory segmentation method is provided, comprising:

[0006] S100, obtaining ADS-B data corresponding to the trajectory to be processed to obtain a corresponding data list S = (S1, S2, ..., S i , …, S n ); i = 1, 2, ..., n; where n is the number of ADS-B data corresponding to the trajectory to be processed; S i is the i-th ADS-B data corresponding to the trajectory to be processed; each ADS-B data has a corresponding time and flight altitude; S is arranged in chronological order;

[0007] S200, determining a target trajectory point on the trajectory to be processed according to S; wherein the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest;

[0008] S300, obtaining two target split points according to the binary method, the target trajectory point and the trajectory to be processed; wherein the value of the speed-altitude determinant corresponding to all ADS-B data between the two target split points is less than a preset determinant threshold;

[0009] S400, dividing the flight trajectory according to the two segmentation points to obtain a climbing phase, a cruising phase and a descending phase.

[0010] In a second aspect of the present application, a flight trajectory segmentation device is provided, and the device includes:

[0011] A data acquisition unit, configured to acquire ADS-B data corresponding to a trajectory to be processed, so as to obtain a corresponding data list S = (S1, S2,..., S i ,..., S n ); i = 1, 2,..., n; where n is the number of ADS-B data corresponding to the trajectory to be processed; S i is the i-th ADS-B data corresponding to the trajectory to be processed; each ADS-B data has a corresponding time and flight altitude; S is arranged in chronological order;

[0012] A trajectory point determination unit, configured to determine a target trajectory point on the trajectory to be processed according to S; where the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest;

[0013] A segmentation point acquisition unit, configured to obtain two target segmentation points according to the dichotomy method, the target trajectory point, and the trajectory to be processed; where the value of the velocity-altitude determinant corresponding to all ADS-B data between the two target segmentation points is less than a preset determinant threshold;

[0014] A segmentation unit, configured to segment the flight trajectory according to the two segmentation points, so as to obtain a climbing stage, a cruising stage, and a descending stage.

[0015] In a third aspect of the present application, a non-transitory computer-readable storage medium is provided, and at least one instruction or at least one program segment is stored in the storage medium, and the at least one instruction or at least one program segment is loaded and executed by a processor to implement the foregoing flight trajectory segmentation method.

[0016] In a fourth aspect of the present application, an electronic device is provided, including a processor and the foregoing non-transitory computer-readable storage medium.

[0017] The present application has at least the following beneficial effects:

[0018] The flight trajectory segmentation method provided by this application first determines the target trajectory point with the highest flight altitude in the ADS-B data. This point is most likely in the middle stage of the entire cruise phase. Then, based on this point and the start point and end point of the trajectory to be processed, the bisection method is used to obtain two target segmentation points. The cruise phase obtained by division is between the two target segmentation points. If the value of the velocity-altitude determinant corresponding to all ADS-B data between the two target segmentation points is less than the preset determinant threshold, it indicates that all ADS-B data between the two target segmentation points is almost in a collinear state. Under normal circumstances, it should be 0. However, due to the fluctuations in the altitude and speed of the aircraft during flight, the value of the corresponding velocity-altitude determinant is less than the preset determinant threshold. Finally, based on the two segmentation points, the flight trajectory is segmented to obtain the climb phase, cruise phase, and descent phase. This application has a high degree of automation, saves manpower, and the segmentation result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a flowchart of the flight trajectory segmentation method provided by the embodiment of this application;

[0021] Figure 2 It is a structural block diagram of the flight trajectory segmentation system provided by the embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some, rather than all, embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] It should be noted that the following describes various aspects of embodiments within the scope of the appended claims. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on this application, 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 aspects described herein can be used to implement a device and / or practice a method. In addition, this device and / or practice of this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0025] Please refer to Figure 1 As shown, an embodiment of this application provides a flight trajectory segmentation method, and the method includes:

[0026] S100, obtain the ADS-B data corresponding to the trajectory to be processed to obtain the corresponding data list S = (S1, S2,..., S i ,..., S n ); i = 1, 2,..., n; where n is the number of ADS-B data corresponding to the trajectory to be processed; S i is the i-th ADS-B data corresponding to the trajectory to be processed; each ADS-B data has a corresponding time and flight altitude; S is arranged in chronological order.

[0027] Specifically, the trajectory to be processed is a flight trajectory obtained by a certain aircraft during a certain flight mission, which has corresponding several ADS-B data, and each ADS-B data has a corresponding time and flight altitude.

[0028] S200, determine a target trajectory point on the trajectory to be processed according to S; where the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest.

[0029] S300. Obtain two target segmentation points according to the dichotomy method, the target trajectory points and the trajectory to be processed. Among them, the value of the velocity-altitude determinant corresponding to all ADS-B data between the two target segmentation points is less than a preset determinant threshold.

[0030] S400. Segment the flight trajectory according to the two segmentation points to obtain a climb phase, a cruise phase, and a descent phase.

[0031] In this embodiment, determine the target trajectory point with the highest flight altitude in the ADS-B data. This point is probably in the middle stage of the entire cruise phase. Furthermore, according to this point and the start point and end point of the trajectory to be processed, use the dichotomy method to obtain two target segmentation points. The cruise phase obtained by division is between the two target segmentation points. And the value of the velocity-altitude determinant corresponding to all ADS-B data between the two target segmentation points is less than a preset determinant threshold, which indicates that all ADS-B data between the two target segmentation points is almost in a collinear state. Under normal circumstances, it should be 0. However, due to the fluctuations of the aircraft's altitude and speed during flight, the value of its corresponding velocity-altitude determinant is less than the preset determinant threshold. Finally, segment the flight trajectory according to the two segmentation points to obtain a climb phase, a cruise phase, and a descent phase. This embodiment has a high degree of automation and saves manpower. When the data noise of the trajectory to be processed is small, using this method to segment the aircraft flight trajectory has a better effect and the segmentation result is more accurate.

[0032] In an exemplary embodiment of the present application, each target segmentation point is determined through the following steps:

[0033] S310. Determine the trajectory to be processed between the target trajectory point and any endpoint of the trajectory to be processed as the key trajectory. Specifically, the trajectory to be processed has two endpoints: a start point and an end point. As an example, determine the trajectory to be processed between the target trajectory point and the end point of the trajectory to be processed as the key trajectory.

[0034] S320. Obtain the key trajectory points corresponding to the key trajectory. Among them, the key trajectory point is the trajectory point corresponding to the ADS-B data with the smallest time difference from the intermediate time point among all ADS-B data included in the key trajectory. The key trajectory includes a first key trajectory and a second key trajectory. The critical point of the first key trajectory and the second key trajectory is the key trajectory point. The first key trajectory is close to the target trajectory point. The second trajectory point is far from the target trajectory point.

[0035] Specifically, the key trajectory point is the trajectory point corresponding to a certain ADS-B data on the key trajectory, and this ADS-B data is the ADS-B data with the smallest time difference from the intermediate time point among all the ADS-B data included in the key trajectory. Here, the intermediate time point is the intermediate time point between the time of the ADS-B data corresponding to the target trajectory point and the time of the ADS-B data corresponding to the end point of the trajectory to be processed. However, due to possible delays in ADS-B data, there may be no ADS-B data at this intermediate time point. In this case, the trajectory point corresponding to the ADS-B data with the smallest time difference from the intermediate time point, that is, the ADS-B data closest to the intermediate time point on the key trajectory, is determined as the key trajectory point. That is, the first "divide into two" is completed to obtain the first key trajectory and the second key trajectory. Among them, the critical points of the first key trajectory and the second key trajectory are the key trajectory points; the first key trajectory is close to the target trajectory point; the second trajectory point is far from the target trajectory point.

[0036] S330. According to the target trajectory point and the key trajectory point, obtain the speed-altitude array list Q = (Q1, Q2,..., Q s ,..., Q t ); s = 1, 2,..., t; t is the number of ADS-B data corresponding to the trajectory to be processed between the target trajectory point and the key trajectory point; Q s is the s-th speed-altitude array; Q s = (v s , h s ); v s is the speed corresponding to the s-th speed-altitude array; h s is the flight altitude corresponding to the s-th speed-altitude array.

[0037] Specifically, the reason for obtaining the speed-altitude array corresponding to the trajectory to be processed between the target trajectory point and the key trajectory point is that the trajectory to be processed near the target trajectory point is more likely to be the trajectory corresponding to the cruise phase than the trajectory to be processed far from the target trajectory point. Therefore, in this embodiment, the speed-altitude array list corresponding to this section is obtained.

[0038] S340. According to Q, obtain the corresponding speed-altitude determinant

[0039] In S350, if SG is less than or equal to a preset determinant threshold, the second critical trajectory is determined as the critical trajectory; otherwise, the first critical trajectory is determined as the critical trajectory; and then jump to step S320; until the target segmentation point is obtained; where the value of the speed-altitude determinant corresponding to all ADS-B data between the target trajectory point and the target segmentation point is less than or equal to the preset determinant threshold; and the value of the determinant between the target trajectory point and the trajectory point corresponding to the adjacent ADS-B data in the direction away from the target trajectory point is greater than the preset determinant threshold.

[0040] Specifically, if SG is less than or equal to the preset determinant threshold, it indicates that all ADS-B data included in this segment are almost collinear, that is, this segment is all part of the cruise phase. At this time, the other segment (between the critical trajectory point and the end point) is determined as the critical trajectory for binary division, and the position of the critical trajectory point is updated. Then, the speed-altitude determinant between the target trajectory point and the critical trajectory point is obtained again and it is judged whether they are close to collinearity. If so, continue to determine the segment between the critical trajectory point and the end point as the critical trajectory for binary division. If not, perform binary division on this segment until the target segmentation point is obtained. After determining the segmentation point between the target trajectory point and the end point, further, use the same method to determine the segmentation point between the target trajectory point and the start point.

[0041] In this embodiment, the binary method and the speed-altitude determinant are used to start from the target trajectory point and determine a target segmentation point on each side of the target trajectory point respectively. Compared with trying one by one ADS-B data, the efficiency is higher and more time is saved.

[0042] After step S100, the method further includes:

[0043] S500, according to S, obtain the data noise Z corresponding to the trajectory to be processed.

[0044] S600, if Z is greater than the preset data noise threshold, jump to step S200.

[0045] Specifically, if Z is less than or equal to the preset data noise threshold, it indicates that the data noise corresponding to the trajectory to be processed is small and there is no need to perform denoising processing. Using the above method has a high degree of automation and saves manpower. When the data noise of the trajectory to be processed is small, using the above method for segmenting the aircraft flight trajectory has a better effect and the segmentation result is more accurate.

[0046] In an exemplary embodiment of the present application, Z meets the following conditions:

[0047]

[0048] where, t n is the time corresponding to the nth ADS-B data; ti is the time corresponding to the i-th ADS-B data; v(t i ) is the speed corresponding to the i-th ADS-B data; is the window average speed corresponding to the i-th ADS-B data.

[0049] In this embodiment, only the speed noise is used as the noise of the trajectory to be processed, which is simple to calculate and improves the calculation efficiency.

[0050] In an exemplary embodiment of the present application, Z meets the following conditions:

[0051]

[0052] where h(t i ) is the altitude corresponding to the i-th ADS-B data; is the window average altitude corresponding to the i-th ADS-B data.

[0053] In this embodiment, only the altitude noise is used as the noise of the trajectory to be processed, which is simple to calculate and improves the calculation efficiency.

[0054] In an exemplary embodiment of the present application, Z meets the following conditions:

[0055]

[0056] In this embodiment, the data noise includes speed noise and altitude noise. Among them, the speed noise and altitude noise refer to the unstable and irregular fluctuation phenomena of speed and altitude data, as if there is "noise" interference in the data. Compared with the above embodiment, the data noise determined in this embodiment is more accurate and comprehensive.

[0057] It should be noted that is the window average speed corresponding to the i-th ADS-B data, is the arithmetic mean from the (i - N / 2)-th point to the (i + N / 2)-th point; N is the number of ADS-B data included in the preset window; as an example: when N is 8 and i is 5, the window average speed corresponding to the 5th ADS-B data is the arithmetic mean of the speed corresponding to the 1st ADS-B data to the speed corresponding to the 5th ADS-B data; is the window average altitude corresponding to the i-th ADS-B data, is the arithmetic mean from the (i - N / 2)-th point to the (i + N / 2)-th point; as an example: when N is 8 and i is 5, the window average altitude corresponding to the 5th ADS-B data is the arithmetic mean of the altitude corresponding to the 1st ADS-B data to the altitude corresponding to the 5th ADS-B data.

[0058] After step S500, the method further includes:

[0059] S700, if Z is greater than a preset data noise threshold, obtain a preset basic cruise altitude; wherein, the preset basic cruise altitude is determined according to the flight altitude corresponding to each ADS-B data in S; the preset basic cruise altitude is less than the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data.

[0060] Specifically, if Z is greater than the preset data noise threshold, it indicates that the corresponding data noise in the to-be-processed trajectory is relatively large. At this time, the flight stage of the to-be-processed trajectory is divided according to the preset basic cruise altitude. Here, the preset basic cruise altitude is determined according to the flight altitude corresponding to each ADS-B data in S, and it is the flight altitude corresponding to the ADS-B data that may be the cruise altitude after removing the noise data among many ADS-B data. Among them, the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data may be noise data.

[0061] S800, divide the to-be-processed trajectory into flight stages according to the preset basic cruise altitude; wherein, the flight stages include a climbing stage, a cruising stage, and a descending stage.

[0062] Specifically, dividing the to-be-processed trajectory into flight stages according to the preset basic cruise altitude removes some noise data. In the case where the overall data has a large amount of noise, a method of screening out some noise and then dividing the flight stages is selected. This improves the accuracy of flight stage division.

[0063] In this embodiment, the obtained preset cruise altitude is data after removing some noise, which can improve the accuracy of flight stage division for the case where the noise in the trajectory is relatively large.

[0064] In an exemplary embodiment of the present application, step S700 includes:

[0065] S710, sort the flight altitudes corresponding to each ADS-B data in S in descending order to obtain a flight altitude list h = (h1, h2,..., h a ,..., h n ); a = 1, 2,..., n; where h a is the flight altitude ranked at the a-th position.

[0066] S720, obtain a preset number of flight altitudes in h in descending order to obtain a key flight altitude list Gh = (Gh1, Gh2,..., Gh x ,..., Gh y ); x = 1, 2,..., y; where y is the preset number; Gh xis the x-th critical flight altitude.

[0067] S730, determine MIN(Gh) as the preset basic cruise altitude; where MIN() is a preset minimum value determination function.

[0068] Specifically, in this embodiment, the flight altitudes corresponding to each ADS-B data in S are sorted in descending order to obtain an ordered flight altitude list, and the smallest flight altitude among the top preset number of flight altitudes selected in the ordered flight altitude list is used as the preset basic cruise altitude. That is, it is considered that the ADS0-B data corresponding to the higher several flight altitudes may be noise. Further, in order to improve the accuracy of determining the preset basic cruise altitude, in this embodiment, y conforms to the following characteristics:

[0069] y = (n / L) × (n / 10000);

[0070] where L is the flight distance corresponding to the trajectory to be processed.

[0071] Here, y is the preset number, and the magnitude of the number of y is the amount of the so-called noise altitude. Here, y is proportional to n / L, and n / L represents the density of the ADS-B data corresponding to the trajectory to be processed; then, the greater the density of the ADS-B data, the greater the possible number of noises. Further, the number of y is adjusted according to n / 10000. That is, under the condition that L is constant, the greater n is, the greater the impact on the number of noises, which is a square multiple increase, and the impact of the flight distance on the number of noise altitudes is less than the impact of the ADS-B data quantity on the number of noises.

[0072] In an exemplary embodiment of the present application, step S800 includes:

[0073] S810, according to S, obtain the altitude difference list C = (C1, C2,..., C i ,..., C n ); where C i is the i-th altitude difference; C i = |α × MIN(Gh) - h(t i )|; α is the altitude adjustment parameter; 0 < α < 1.

[0074] Specifically, obtain the altitude difference between α × MIN(Gh) and each flight altitude. Here, α is the altitude adjustment parameter. That is, in order to ensure that the obtained cruise altitude is more accurate, the altitude adjustment parameter is set. On the basis of the preset basic cruise altitude, a more accurate possible cruise altitude is further generated, and α meets the following conditions:

[0075] α = α' × β;

[0076] α’ is the basic height adjustment parameter; β is the fluctuation adjustment parameter.

[0077] Among them, β is determined according to the following steps:

[0078] S001. Obtain the height corresponding to each ADS-B data in the cruise stage of the historical trajectory obtained when the executing aircraft corresponding to the trajectory to be processed executes the flight route corresponding to the trajectory to be processed within the historical time window, so as to obtain the historical height list set Lh = (Lh1, Lh2,..., Lh e , …, Lh k ); e = 1, 2, …, k; where k is the number of historical trajectories obtained when the executing aircraft corresponding to the trajectory to be processed executes the flight route corresponding to the trajectory to be processed within the historical time window; Lh e is the height list corresponding to the historical trajectory obtained when the executing aircraft corresponding to the trajectory to be processed executes the flight route corresponding to the trajectory to be processed for the e-th time within the historical time window in the cruise stage; Lh e = (Lh e，1 , Lh e，2 , …, Lh e，r , …, Lh e，f(e) ); r = 1, 2, …, f(e); f(e) is the number of ADS-B data included in Lh e ; Lh e，r is the height corresponding to the r-th ADS-B data included in Lh e .

[0079] S002. According to Lh, obtain the historical average fluctuation value PB of the trajectory to be processed = avg((1 / f(e))Σ f(e) r＝1 (Lh e，r - avg(Lh e )) 2 ); where avg() is a preset average value determination function.

[0080] S003. If PB is greater than the preset fluctuation value threshold, then determine β < 1, otherwise determine β > 1.

[0081] In this embodiment, by setting a fluctuation adjustment parameter, the final value of the altitude adjustment parameter is determined. The fluctuation adjustment parameter is determined according to each ADS-B data corresponding to the historical trajectory during the cruise phase when the executing aircraft corresponding to the trajectory to be processed executes the flight route corresponding to the trajectory to be processed within the historical time window. That is, according to Lh, the historical average fluctuation value corresponding to the trajectory to be processed is obtained. If PB is large, it means that the altitude fluctuation of the aircraft during the cruise phase is large when the executing aircraft corresponding to the trajectory to be processed executes the flight route corresponding to the trajectory to be processed within the historical time window, exceeding the preset fluctuation value threshold. Then, it means that the altitude fluctuation of the trajectory to be processed may also be large during the cruise phase. At this time, in order to ensure the accuracy of flight phase segmentation, the cruise altitude needs to be set a little lower, so β < 1. On the contrary, if PB is less than or equal to the preset fluctuation value threshold, it means that the cruise altitude needs to be set a little higher, so β > 1 at this time.

[0082] This embodiment enables the finally determined cruise altitude to consider the influence of noise and filter out the influence of part of the noise when there is noise. In order to make the determined noise more accurate, historical data is introduced to obtain the altitude fluctuation of the aircraft corresponding to the trajectory to be processed during the cruise phase when the aircraft corresponding to the trajectory to be processed executes the flight route corresponding to the trajectory to be processed within the historical time window, so as to adjust the cruise altitude and make the finally determined cruise altitude more accurate.

[0083] S820. According to C, obtain the key altitude difference list GC = (GC1, GC2,..., GC c ,..., GC d ); c = 1, 2,..., d; where d is the number of key altitude differences; GC c is the c-th key altitude difference; GC c is less than the preset altitude difference threshold.

[0084] Specifically, after determining the most likely cruise altitude, obtain several ADS-B data whose altitude differences from this cruise altitude are less than the preset altitude difference threshold. These are all the ADS-B data included in the cruise phase.

[0085] S830. Determine the position corresponding to the earliest time in GC in the trajectory to be processed as the first division point, and the position corresponding to the latest time in GC in the trajectory to be processed as the second division point.

[0086] Specifically, determine the position corresponding to the earliest time in GC in the trajectory to be processed as the first division point, and the position corresponding to the latest time in GC in the trajectory to be processed as the second division point. Here, the first division point is the critical point between the climb phase and the cruise phase; the second division point is the critical point between the cruise phase and the descent phase.

[0087] S840. Divide the trajectory to be processed according to the first division point and the second division point, where the section from the starting point of the trajectory to be processed to the first division point is the climbing stage; the section from the first division point to the second division point is the cruising stage; and the section from the second division point to the end point of the trajectory to be processed is the descending stage.

[0088] In an exemplary embodiment of the present application, both of the above two segmentation methods divide the flight stage after obtaining the complete trajectory as the trajectory to be processed after the flight ends. The flight stage can also be divided during the flight, including the following steps:

[0089] S010. In response to receiving the ADS-B data returned by the target aircraft executing the flight mission, obtain the speed-altitude array list ZS=(ZS1, ZS2,..., ZS v ,..., ZS w ) corresponding to the target aircraft executing the flight mission; v = 1, 2,..., w; where w is the number of ADS-B data in the same flight stage received by the target aircraft executing the flight mission up to the current time; ZS v is the speed-altitude array corresponding to the v-th ADS-B data in the same flight stage received by the target aircraft executing the flight mission up to the current time; ZS has a corresponding segmentation point recognition result sequence QF=(QF1, QF2,..., QF v ,..., QF w ); where QF v is the segmentation point recognition result corresponding to ZS v ; QF v =0 or QF v =1; where QF v =0 indicates that the ADS-B data corresponding to ZS v is not a flight stage segmentation point; QF v =1 indicates that the ADS-B data corresponding to ZS v is a flight stage segmentation point.

[0090] Specifically, every time the ADS-B data returned by the target aircraft executing the flight mission is received, the corresponding speed-altitude array list ZS is obtained. Here, all the arrays in the speed-altitude array list are arrays in the same flight stage state. As an example: If the first target segmentation point has been determined, when determining the second target segmentation point, the corresponding speed-altitude data list is not obtained from the start of the flight, but from after the first target segmentation point. That is, at this time, the flight stages corresponding to the speed-altitude arrays included in the corresponding speed-altitude array list are all the cruising stage. If the first target segmentation point has not been determined, the speed-altitude array is obtained from the takeoff of the flight.

[0091] Further, ZS has a corresponding segmentation point recognition result sequence QF. Each recognition result in QF is the recognition result obtained by the HMM model on whether the ADS-B data is a target segmentation point. Among them, QF v = 0 indicates that the ADS-B data corresponding to ZS v is not a flight phase segmentation point; QF v = 1 indicates that the ADS-B data corresponding to ZS v is a flight phase segmentation point.

[0092] S020, input ZS and QF into the HMM model to obtain a prediction result and mark it until two predicted target segmentation points are obtained, then the prediction ends; among them, the prediction result is used to indicate whether an ADS-B data received after the current time is a flight phase segmentation point; ZS is the observation sequence of the HMM model; QF is the hidden sequence of the HMM model.

[0093] Specifically, input the above ZS and QF into the HMM model, where ZS is the observation sequence of the HMM model; QF is the hidden sequence of the HMM model. According to the HMM model, obtain the prediction result corresponding to an ADS-B data received after the current time. Here, the prediction result indicates whether an ADS-B data received after the current time is a target segmentation point.

[0094] The HMM model is the Hidden Markov Model. Here, the HMM model is trained based on the complete trajectories to be processed that have been completed during several historical times, and each ADS-B data on the trajectory has been labeled (whether it is a target segmentation point). It has corresponding parameters: the initial state probability vector, the state transition probability matrix, and the observation probability matrix. Among them, the initial state probability vector represents the probability of being in each hidden state at the initial moment, providing the initial conditions for the subsequent state transitions and the generation of observation values. The state transition probability matrix describes the probability of transitioning between different hidden states, depicting the law of the change of hidden states over time, which is the core of the dynamic change of the HMM model. The observation probability matrix gives the probability of generating each observation value in each hidden state, establishing the connection between the hidden state and the observation value, enabling us to infer the hidden state through the observed data.

[0095] In the present application, the observed values of the above model include the speed and time in each ADS-B data because during the flight climb or descent process, there is a process of climb - level flight - climb - level flight - climb, or descent - level flight - descent - level flight. During this process, the speed and altitude are mutually restrictive. In this embodiment, the HMM model is used as the prediction model because the HMM model has a learning function. During the level flight stage of the climb or descent phase, since the relationship between its corresponding speed and time is different from that in the cruise stage, the HMM model will not identify the process of climb - level flight - climb - level flight - climb or descent - level flight - descent - level flight during the flight climb or descent as a cruise process, making the predicted result more accurate. Therefore, in this embodiment, the speed and altitude are used as the observation sequence of the HMM model, which is more convenient for the HMM model to predict the hidden state corresponding to the next ADS-B data. Due to the learning ability of the HMM model, the predicted result is more accurate.

[0096] In an exemplary embodiment of the present application, it is also possible to input only ZS into the model to obtain a prediction result and perform marking until two predicted target segmentation points are obtained, and then the prediction ends.

[0097] In an exemplary embodiment of the present application, after step S010, the method further includes:

[0098] S030, input ZS and QF into the HMM model. When a target segmentation point is predicted, for each ADS-B data returned by a target aircraft received, obtain the corresponding key speed - altitude array list GQ = (GQ1, GQ2,..., GQ z ,..., GQ η ); z = 1, 2,..., η; where η is the number of ADS-B data returned by the target aircraft currently received when a target segmentation point is predicted; GQ z is the speed - altitude array corresponding to the z-th ADS-B data returned by the target aircraft currently received when a target segmentation point is predicted; GQ z = (gv z , gh z ); gv z is the speed corresponding to GQ z ; gh z is the altitude corresponding to GQ z .

[0099] S040, according to GQ, obtain the corresponding key speed - altitude determinant

[0100] S050. If GH is greater than a preset determinant threshold, then delete GQ1, update GQ, and jump to step S400; until GH is less than or equal to the preset determinant threshold; and determine GQ1 as the target segmentation point.

[0101] In this embodiment, when a target segmentation point is predicted, the target segmentation point is the segmentation point between the climbing stage and the cruise stage predicted by the HMM model. However, due to various reasons, the ADS-B data during the flight phase may be delayed or for other reasons. After determining the target segmentation point, the ADS-B data after this target segmentation point is used as the observation sequence for predicting the next target segmentation point. If the predicted target segmentation point is too early, that is, it has not reached the actual target ball segmentation point, but the HMM model determines the trajectory point corresponding to the ADS-B data before the actual target segmentation point as the target segmentation point, then the first part of the subsequent observation states contains the speed-altitude data corresponding to part of the climbing stage, which will cause inaccurate prediction of the next target segmentation point. Therefore, after obtaining the first target segmentation point predicted by the HMM model, it needs to be further verified. The determination method is to check whether each speed-altitude array corresponding to the ADS-B from the target segmentation point to the latest ADS-B data obtained at the current time is approximately collinear. If it is continuously approximately collinear (GH is less than or equal to the preset determinant threshold), it means that the speed-altitude data of the climbing stage is not included after the first target segmentation point obtained by the HMM model, and all are data of the cruise stage. On the contrary, if it is not approximately collinear (GH is greater than the preset determinant threshold), it means that the speed-altitude data of part of the climbing stage is included after the first target segmentation point obtained by the HMM model. At this time, this part of the data should be deleted. GQ1 can be deleted in sequence to obtain the updated GQ, or the bisection method can be used to re-determine the target segmentation point. The speed-altitude determinants formed by the ADS-B data between the re-determined target segmentation point and the next target segmentation point should all be approximately collinear (GH is less than or equal to the preset determinant threshold). Therefore, this embodiment verifies the first target segmentation point determined by the HMM model and makes timely corrections to avoid affecting the accuracy of the second target segmentation point determined by the HMM model.

[0102] Please refer to Figure 2 As shown, an embodiment of the present application provides a flight trajectory segmentation device 100, and the device includes

[0103] A data acquisition unit 110, configured to acquire ADS-B data corresponding to the trajectory to be processed to obtain a corresponding data list S = (S1, S2,..., S i ,..., S n ); i = 1, 2,..., n; where n is the number of ADS-B data corresponding to the trajectory to be processed; S iThe i-th ADS-B data corresponding to the trajectory to be processed; each ADS-B data has a corresponding time and flight altitude; S is arranged in chronological order.

[0104] The trajectory point determination unit 120 is configured to determine a target trajectory point on the trajectory to be processed according to S; wherein, the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest.

[0105] The segmentation point acquisition unit 130 is configured to obtain two target segmentation points according to the dichotomy method, the target trajectory point and the trajectory to be processed; wherein, the value of the velocity-altitude determinant corresponding to all the ADS-B data between the two target segmentation points is less than a preset determinant threshold.

[0106] The segmentation unit 140 is configured to segment the flight trajectory according to the two segmentation points to obtain a climbing phase, a cruising phase and a descending phase.

[0107] Those skilled in the art of the present application can understand that various aspects of the present application can be implemented as a system, a method or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module" or "system" here.

[0108] An electronic device according to this embodiment of the present application. The electronic device is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0109] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the above-mentioned at least one processor, the above-mentioned at least one storage, and a bus connecting different system components (including the storage and the processor).

[0110] Among them, the storage stores program codes, and the program codes can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present application described in the above "exemplary method" section of this specification.

[0111] The storage may include a readable medium in the form of a volatile storage, such as a random access storage (RAM) and / or a cache storage, and may further include a read-only storage (ROM).

[0112] The storage may further include a program / utility having a set of (at least one) program modules, and such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0113] The bus can represent one or more of several types of bus architectures, including a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of the various bus architectures.

[0114] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), can also communicate with one or more devices that enable a user to interact with the electronic device, and / or can communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through the bus. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination 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, etc.

[0115] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0116] In an exemplary embodiment of the present application, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of this specification is stored. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0117] The program product may employ any combination of one or more readable media. The readable media 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 of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium 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 foregoing.

[0118] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0119] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0120] The program code for performing the operations of this application may be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone 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 the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0121] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes may be executed, for example, synchronously or asynchronously in multiple modules.

[0122] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] The above is only the specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A flight trajectory segmentation method, characterized in that, The method includes: S100. Obtain the ADS-B data corresponding to the trajectory to be processed to obtain the corresponding data list S = (S1, S2, …, S i , …, S n ); i = 1, 2, …, n; where n is the number of ADS-B data corresponding to the trajectory to be processed; S i is the i-th ADS-B data corresponding to the trajectory to be processed; each ADS-B data has a corresponding time and flight altitude; S is arranged in chronological order; S200. Determine a target trajectory point on the trajectory to be processed according to S, where the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest. S300. Obtain two target segmentation points according to the bisection method, the target trajectory point and the trajectory to be processed, where the value of the velocity-altitude determinant corresponding to all ADS-B data between the two target segmentation points is less than a preset determinant threshold. S400. Segment the flight trajectory according to the two segmentation points to obtain a climb phase, a cruise phase, and a descent phase.

2. The flight trajectory segmentation method according to claim 1, wherein Each target segmentation point is determined through the following steps: S310. Determine the trajectory to be processed between the target trajectory point and any endpoint of the trajectory to be processed as the key trajectory. S320. Obtain the key trajectory points corresponding to the key trajectory, where the key trajectory points are the trajectory points on the key trajectory corresponding to the ADS-B data with the smallest time difference from the intermediate time point among all ADS-B data included in the key trajectory. The key trajectory includes a first key trajectory and a second key trajectory, and the critical point of the first key trajectory and the second key trajectory is the key trajectory point. The first key trajectory is close to the target trajectory point, and the second trajectory point is far from the target trajectory point. S330, according to the target trajectory point and the key trajectory point; obtain the speed-altitude array list Q = (Q1, Q2, …, Q s , …, Q t ); s = 1, 2, …, t; t is the number of ADS-B data corresponding to the to-be-processed trajectory between the target trajectory point and the key trajectory point; Q s is the s-th speed-altitude array; Q s = (v s , h s ); v s is the speed corresponding to the s-th speed-altitude array; h s is the flight altitude corresponding to the s-th speed-altitude array; S340, obtain the corresponding speed-altitude determinant according to Q S350. If SG is less than or equal to the preset determinant threshold, determine the second key trajectory as the key trajectory; otherwise, determine the first key trajectory as the key trajectory, and jump to step S320 until the target segmentation point is obtained. The value of the velocity-altitude determinant corresponding to all ADS-B data between the target trajectory point and the target segmentation point is less than or equal to the preset determinant threshold, and the value of the determinant between the target trajectory point and the trajectory point of the adjacent ADS-B data in the direction away from the target trajectory point of the target segmentation point is greater than the preset determinant threshold.

3. The flight trajectory segmentation method according to claim 1, characterized in that After step S100, the method further includes: S500. Obtain the data noise Z corresponding to the trajectory to be processed according to S. S600. If Z is greater than the preset data noise threshold, jump to step S200.

4. The flight trajectory segmentation method according to claim 3, wherein Z meets the following conditions: where t n is the time corresponding to the nth ADS - B data; t i is the time corresponding to the ith ADS - B data; v(t i ) is the speed corresponding to the ith ADS - B data; is the window average speed corresponding to the ith ADS - B data.

5. The flight trajectory segmentation method according to claim 3, characterized in that, Z meets the following conditions: where h(t i ) is the altitude corresponding to the i-th ADS-B data; is the window-averaged altitude corresponding to the i-th ADS-B data.

6. The flight trajectory segmentation method according to claim 3, wherein Z meets the following conditions:

7. A flight trajectory segmentation device, characterized in that, The device includes: A data acquisition unit, configured to acquire ADS-B data corresponding to a trajectory to be processed, so as to obtain a corresponding data list S = (S1, S2, …, S i , …, S n ); i = 1, 2, …, n; where n is the quantity of ADS-B data corresponding to the trajectory to be processed; S i is the i-th ADS-B data corresponding to the trajectory to be processed; each ADS-B data has a corresponding time and flight altitude; S is arranged in chronological order; A trajectory point determination unit, configured to determine a target trajectory point on the trajectory to be processed according to S, where the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest. A segmentation point acquisition unit, configured to obtain two target segmentation points according to the bisection method, the target trajectory point, and the trajectory to be processed, where the value of the velocity-altitude determinant corresponding to all ADS-B data between the two target segmentation points is less than a preset determinant threshold. A segmentation unit, configured to segment the flight trajectory according to the two segmentation points to obtain a climb phase, a cruise phase, and a descent phase.

8. A non-transitory computer-readable storage medium, characterized in that, At least one instruction or at least one segment of program is stored in the storage medium, and the at least one instruction or the at least one segment of program is loaded and executed by a processor to implement the method according to any one of claims 1-6.

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

Images