Flight phase determination method and device, medium and equipment

Abstract

The invention provides a flight stage determination 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 track, so as to obtain a corresponding data list S; according to the S, obtaining data noise Z corresponding to the to-be-processed track; if Z is greater than a preset data noise threshold, acquiring a preset basic cruise height; and according to a preset basic cruise height, dividing flight stages of the to-be-processed track. According to the method, the flight stage division is carried out on the to-be-processed track according to the preset basic cruise height, part of noise data is removed, and under the condition that the noise of the whole data is large, the method of screening part of noise and then carrying out flight stage division is selected. And the accuracy of flight stage division is improved.

Description

Background Art

[0002] In the aviation field, it is of great significance to accurately divide the flight phases of aircraft flight trajectories. Flight phase division can provide key data support for flight safety assessment, flight performance analysis, and air traffic management. However, in actual flight, there are many challenges in obtaining aircraft flight trajectories.

[0003] When an aircraft is flying, it will be affected by many complex factors. On the one hand, the aircraft's own sensor system will introduce a certain degree of error when collecting flight data due to hardware accuracy limitations, signal interference, etc. On the other hand, external environmental factors, such as atmospheric turbulence and electromagnetic interference, will also affect the accuracy of flight data. The combined effect of these factors may result in a large amount of noise in the acquired flight trajectory data.

[0004] The presence of noise seriously interferes with the accurate division of flight phases. For example, in the traditional flight phase division method based on flight parameter threshold judgment, noise may cause the flight parameters to exceed or fall below the normal threshold range instantly, resulting in errors in the division results. If the noise cannot be effectively quantified and the appropriate flight phase division method cannot be selected, it will not only reduce the accuracy of the flight phase division, but also may affect the reliability of subsequent analyses and decisions based on the flight phase division, thereby adversely affecting flight safety and aviation operation efficiency. Therefore, there is an urgent need for a technical solution that can effectively quantify noise and select an appropriate flight phase division method to improve the accuracy and reliability of flight trajectory analysis. Summary of the invention

[0005] In response to the above technical problems, the present application provides a flight phase determination method, device, medium and equipment, which at least partially solve the problems existing in the prior art.

[0006] In a first aspect of the present application, a flight phase determination method is provided, the method comprising: S100, obtaining 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, according to S, obtain the data noise Z corresponding to the trajectory to be processed; wherein Z meets the following conditions: ;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; h(t i ) is the altitude corresponding to the i-th ADS-B data; is the average window height corresponding to the i-th ADS-B data; S300, if Z is greater than a preset data noise threshold, obtaining a preset basic cruising altitude; wherein the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S; and the preset basic cruising altitude is less than the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data; S400, dividing the trajectory to be processed into flight phases according to a preset basic cruising altitude; wherein the flight phases include a climbing phase, a cruising phase and a descending phase.

[0007] In a second aspect of the present application, a flight phase determination device is provided, the device comprising: The acquisition unit is used to acquire 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; The noise determination unit is used to obtain the data noise Z corresponding to the trajectory to be processed according to S; wherein Z meets the following conditions: ;t n is the time corresponding to the nth ADS-B data; t i 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; h(t i ) is the altitude corresponding to the i-th ADS-B data; is the average window height corresponding to the i-th ADS-B data; an altitude determination unit, configured to obtain a preset basic cruising altitude if Z is greater than a preset data noise threshold; wherein the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S; and the preset basic cruising altitude is less than the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data; The phase division unit is used to divide the trajectory to be processed into flight phases according to a preset basic cruising altitude; wherein the flight phases include a climbing phase, a cruising phase and a descending phase.

[0008] In a third aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned flight phase determination method.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0010] This application has at least the following beneficial effects: The flight phase determination method provided by the present application first obtains the ADS-B data corresponding to the trajectory to be processed to obtain the corresponding data list S, wherein each ADS-B data has a corresponding time and flight altitude, and then according to the time and flight altitude corresponding to each ADS-B data, the data noise corresponding to the trajectory to be processed is quantified. Here, the data noise includes speed noise and altitude noise, wherein speed noise and altitude noise refer to the unstable and irregular fluctuation phenomenon of speed and altitude data. If Z is greater than the preset data noise threshold, it means that the data noise corresponding to the trajectory to be processed is too large. At this time, the flight phase of the trajectory to be processed is divided according to the preset basic cruising altitude. Here, the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S, and is the flight altitude corresponding to the ADS-B data that may be the cruising altitude selected after removing the noise data from many ADS-B data. Among them, the maximum flight altitude corresponding to the flight altitude corresponding to the ADS-B data may be noise data. According to the preset basic cruising altitude, the trajectory to be processed is divided into flight phases, and some noise data is removed. In the case where the noise of the overall data is large, a method of screening out some noise and then dividing the flight phase is selected. Improved the accuracy of flight phase division. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flow chart of a method for determining a flight phase provided in an embodiment of the present application; Figure 2A structural block diagram of a flight phase determination device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0016] Please refer to Figure 1 As shown, an embodiment of the present application provides a flight phase determination method, the method comprising: S100, obtaining 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 iis 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.

[0017] Specifically, the trajectory to be processed is a flight trajectory obtained by a certain aircraft when performing a certain flight mission, which has a corresponding number of ADS-B data, and each ADS-B data has a corresponding time and flight altitude.

[0018] S200, according to S, obtain the data noise Z corresponding to the trajectory to be processed; wherein Z meets the following conditions: ;t n is the time corresponding to the nth ADS-B data; t i 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; h(t i ) is the altitude corresponding to the i-th ADS-B data; is the average window height corresponding to the i-th ADS-B data.

[0019] Specifically, Z is the data noise corresponding to the trajectory to be processed. Here, the data noise includes speed noise and height noise. The speed noise and height noise refer to the unstable and irregular fluctuations in the speed and height data, as if the data is mixed with "noise" interference.

[0020] It should be noted that is the window average speed corresponding to the i-th ADS-B data, is the arithmetic mean from the iN / 2th point to the i+N / 2th point; N is the number of ADS-B data contained in the preset window; as an example: N is 8, i is 5, then 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 average window height corresponding to the i-th ADS-B data, is the arithmetic mean from the iN / 2th point to the i+N / 2th point; as an example: N is 8, i is 5, then the average window height corresponding to the 5th ADS-B data is the arithmetic mean of the height corresponding to the 1st ADS-B data to the height corresponding to the 5th ADS-B data.

[0021] S300, if Z is greater than a preset data noise threshold, obtaining a preset basic cruising altitude; wherein the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S; and the preset basic cruising altitude is less than the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data; Specifically, if Z is greater than the preset data noise threshold, it means that the corresponding data noise in the trajectory to be processed is relatively large. At this time, the flight phase of the trajectory to be processed is divided according to the preset basic cruising altitude. Here, the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S. It is the flight altitude corresponding to the ADS-B data that may be the cruising altitude selected after removing the noise data from many ADS-B data. Among them, the maximum flight altitude corresponding to the flight altitude of the ADS-B data may be noise data.

[0022] S400, dividing the trajectory to be processed into flight phases according to a preset basic cruising altitude; wherein the flight phases include a climbing phase, a cruising phase and a descending phase.

[0023] Specifically, the trajectory to be processed is divided into flight stages according to the preset basic cruising altitude, and some noise data is removed. When the overall data has large noise, a method of filtering out some noise and then dividing the flight stages is selected, thereby improving the accuracy of the flight stage division.

[0024] In this embodiment, the preset cruising altitude obtained is data after some noise is removed. For the case where there is a lot of noise in the trajectory, the accuracy of the flight phase division can be improved.

[0025] In an exemplary embodiment of the present application, step S300 includes: S310, 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 a.

[0026] S320, obtaining a preset number of flight heights in h in descending order to obtain a key flight height list Gh=(Gh1, Gh2, ..., Gh x , …, Gh y );x=1,2,…,y;where y is the preset number;Gh x is the xth critical flight altitude.

[0027] S330, determining MIN(Gh) as a preset basic cruising altitude; wherein MIN() is a preset minimum value determination function.

[0028] Specifically, in this embodiment, the flight altitude corresponding to each ADS-B data in S is sorted in descending order to obtain an ordered flight altitude list, and the smallest flight altitude among the first preset number of flight altitudes in the ordered flight altitude list is selected as the preset basic cruising altitude. That is, it is considered that the ADS0-B data corresponding to the higher flight altitudes may be noise. Further, in order to improve the accuracy of determining the preset basic cruising altitude, in this embodiment, y meets the following characteristics: y=(n / L)×(n / 10000); Wherein, L is the flight distance corresponding to the trajectory to be processed.

[0029] Here, y is a preset number, and the size of y is the amount of noise height that is considered. Here, y is proportional to n / L, and n / L represents the density of ADS-B data corresponding to the trajectory to be processed; then, the greater the density of ADS-B data, the greater the amount of noise may be. Further, the number of y is adjusted according to n / 10000, that is, under the condition that L is constant, the larger n is, the greater the impact on the amount of noise, which increases exponentially, and the impact of flight distance on the number of noise heights is smaller than the impact of the number of ADS-B data on the number of noise.

[0030] In an exemplary embodiment of the present application, step S400 includes: S410, according to S, obtain the height difference list C = (C1, C2, ..., C i , …, C n ), where C i is the i-th height difference; C i =|α×MIN(Gh)-h(t i )|; α is the height adjustment parameter; 0<α<1.

[0031] Specifically, the height difference between α×MIN(Gh) and each flight altitude is obtained, where α is an altitude adjustment parameter, that is, in order to ensure that the obtained cruising altitude is more accurate, an altitude adjustment parameter is set, and a more accurate possible cruising altitude is further generated on the basis of the preset basic cruising altitude, and α meets the following conditions: α = α' × β; α' is the basic height adjustment parameter; β is the fluctuation adjustment parameter.

[0032] Among them, β is determined according to the following steps: S001, obtaining the altitude corresponding to each ADS-B data of the historical trajectory in 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, so as to obtain the historical altitude 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 Lh is the altitude list corresponding to the historical trajectory in the cruise phase 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 eth time within the historical time window; e =(Lh e，1 , Lh e，2 ,…,Lh e，r ,…,Lh e，f（e） ); r = 1, 2, ..., f (e); f (e) is Lh e The amount of ADS-B data included; Lh e，r For Lh e The altitude corresponding to the rth ADS-B data included.

[0033] S002, according to Lh, obtain the historical average fluctuation value corresponding to the trajectory to be processed ; Among them, avg() is the preset average value determination function.

[0034] S003, if PB is greater than a preset fluctuation value threshold, determine that β<1, otherwise determine that β>1.

[0035] In this embodiment, the final value of the altitude adjustment parameter is determined by setting a fluctuation adjustment parameter, wherein the fluctuation adjustment parameter is determined according to each ADS-B data corresponding to the historical trajectory in the cruise stage 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 in the cruise stage 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 is large, 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 stage. At this time, in order to ensure the accuracy of the flight stage segmentation, the cruising altitude needs to be set smaller, so β<1. On the contrary, if PB is less than or equal to the preset fluctuation value threshold, it means that the cruising altitude needs to be set larger, so at this time β>1. In order to highlight the regulatory role of β in ensuring the accuracy of flight stage segmentation in this application, β≠1 is determined.

[0036] This embodiment allows the cruising altitude to be finally determined to take into account the influence of noise, and to filter out some of the influence of 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 historical trajectory during the cruising stage 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 cruising altitude, so that the finally determined cruising altitude is more accurate.

[0037] S420, according to C, obtain the key height difference list GC = (GC1, GC2, ..., GC c , …, GC d ), c = 1, 2, ..., d, where d is the number of critical height differences; GC c is the cth key height difference; GC c Less than the preset height difference threshold.

[0038] Specifically, after the most likely cruising altitude is determined, a number of ADS-B data having an altitude difference from the cruising altitude less than a preset altitude difference threshold are obtained, and these are all ADS-B data included in the cruising phase.

[0039] S430, determining the position corresponding to the earliest time in the GC in the trajectory to be processed as the first division point, and determining the position corresponding to the latest time in the trajectory to be processed as the second division point.

[0040] Specifically, the position corresponding to the earliest time in the GC in the trajectory to be processed is determined as the first division point, and the position corresponding to the latest time in the trajectory to be processed is determined as the second division point. Here, the first division point is the critical point between the climbing stage and the cruising stage; the second division point is the critical point between the cruising stage and the descending stage.

[0041] S440, dividing the trajectory to be processed according to the first dividing point and the second dividing point, wherein the period from the starting point of the trajectory to be processed to the first dividing point is the climbing phase; the period from the first dividing point to the second dividing point is the cruising phase; and the period from the second dividing point to the end point of the trajectory to be processed is the descending phase.

[0042] In an exemplary embodiment of the present application, after step S200, the method further includes: S500, if Z is less than or equal to a preset data noise threshold, a target trajectory point is determined on the trajectory to be processed; wherein the flight altitude of the ADS-B data corresponding to the target trajectory point is the highest.

[0043] S600, obtaining two target splitting 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 splitting points is less than a preset determinant threshold.

[0044] S700, dividing the flight trajectory according to the two segmentation points to obtain a climbing phase, a cruising phase and a descending phase.

[0045] In this embodiment, if Z is less than or equal to the preset data noise threshold, it means that the data noise corresponding to the trajectory to be processed is small. At this time, the data is not denoised. First, the target trajectory point with the highest flight altitude in the ADS-B data is determined. This point is most likely in the middle stage of the entire cruise phase. Then, according to this point and the starting point and the end point of the trajectory to be processed, two target dividing points are obtained by using the binary method. The cruise phase obtained by dividing is between the two target dividing points; and the value of the speed-altitude determinant corresponding to all ADS-B data between the two target dividing points is less than the preset determinant threshold, which means that all ADS-B data between the two target dividing points are almost in a collinear state. Under normal circumstances, it should be 0, but because the altitude and speed of the aircraft will fluctuate during the flight, the value of the corresponding speed-altitude determinant is less than the preset determinant threshold. Finally, according to the two segmentation points, the flight trajectory is segmented to obtain the climbing phase, the cruising phase and the descending phase. This embodiment has a high degree of automation and saves manpower. When the data noise of the trajectory to be processed is small, the method is used to segment the flight trajectory of the aircraft, and the effect is better and the segmentation result is more accurate.

[0046] In an exemplary embodiment of the present application, each target segmentation point is determined by the following steps: S610: Determine the trajectory to be processed between the target trajectory point and any end point of the trajectory to be processed as a key trajectory.

[0047] Specifically, the trajectory to be processed has two endpoints: a start point and an end point. As an example: the trajectory to be processed between the target trajectory point and the end point of the trajectory to be processed is determined as a key trajectory.

[0048] S620, obtaining key trajectory points corresponding to the key trajectory; wherein the key trajectory point is a trajectory point on the key trajectory corresponding to the ADS-B data with the smallest time difference with the intermediate time point among all ADS-B data contained in the key trajectory; the key trajectory includes a first key trajectory and a second key trajectory; the critical point between 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 away from the target trajectory point.

[0049] Specifically, the key trajectory point is the trajectory point corresponding to a certain ADS-B data on the key trajectory, and the ADS-B data is the ADS-B data with the smallest time difference from the intermediate time point among all the ADS-B data contained 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, since the ADS-B data may be delayed, etc., there may be no ADS-B data at this intermediate time point. At this time, the ADS-B data with the smallest time difference from the intermediate time point, that is, the trajectory point corresponding to 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 "one-to-two" is completed to obtain the first key trajectory and the second key trajectory, wherein the critical point between 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 away from the target trajectory point.

[0050] S630, 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 sth speed-height array; Q s =(v s ,h s );v s is the speed corresponding to the sth speed-height array; h s is the flight altitude corresponding to the sth speed-altitude array.

[0051] Specifically, the speed-altitude array corresponding to the to-be-processed trajectory between the target trajectory point and the key trajectory point is obtained because the to-be-processed trajectory near the target trajectory point is more likely to be a trajectory corresponding to the cruise stage than the to-be-processed trajectory far from the target trajectory point. Therefore, in this embodiment, the speed-altitude array list corresponding to this section is obtained.

[0052] S640, according to Q, get the corresponding speed-height determinant .

[0053] S650, if SG is less than or equal to the preset determinant threshold, the second key trajectory is determined as the key trajectory; otherwise, the first key trajectory is determined as the key trajectory; and the process jumps to step S320; until the target split point is obtained; wherein the value of the speed-altitude determinant corresponding to all ADS-B data between the target trajectory point and the target split point is less than or equal to the preset determinant threshold; and the value of the determinant between the trajectory points corresponding to the adjacent ADS-B data of the target trajectory point and the target split point in the direction away from the target trajectory point is greater than the preset determinant threshold.

[0054] Specifically, if SG is less than or equal to the preset determinant threshold, it means that all ADS-B data contained in this segment are almost collinear, that is, this segment is part of the cruise phase. At this time, another segment (between the key trajectory point and the end point) is determined as the key trajectory for binary division, and the position of the key trajectory point is updated. The speed-altitude determinant between the target trajectory point and the key trajectory point is obtained again to determine whether it is close to collinear. If so, the key trajectory point and the end point are continued to be determined as the key trajectory for binary division. If not, the segment is binary divided until the target split point is obtained. The split point between the target trajectory point and the end point is determined. Further, the split point between the target trajectory point and the start point is determined using the same method.

[0055] This embodiment uses the binary method and the speed-altitude determinant to take the target trajectory point as the starting point, and determines a target split point on both sides of the target trajectory point. Compared with trying ADS-B data one by one, it is more efficient and saves more time.

[0056] In an exemplary embodiment of the present application, Z meets the following conditions: .

[0057] In this embodiment, only the speed noise is used as the noise of the trajectory to be processed, which simplifies the calculation and improves the calculation efficiency.

[0058] In an exemplary embodiment of the present application, Z meets the following conditions: .

[0059] In this embodiment, only the height noise is used as the noise of the trajectory to be processed, which simplifies the calculation and improves the calculation efficiency.

[0060] In an exemplary embodiment of the present application, the above two segmentation methods are to obtain a complete trajectory after the flight is completed as the trajectory to be processed for flight stage division, and the flight stage division can also be performed during the flight, including the following steps: S010, in response to receiving the ADS-B data returned by the target aircraft that is performing the flight mission, obtaining the speed-altitude array list ZS=(ZS1, ZS2, ..., ZS v , …, ZS w ); v = 1, 2, ..., w; where w is the number of ADS-B data received by the target aircraft in the same flight phase as of the current time; ZS v is the speed-altitude array corresponding to the vth ADS-B data received by the target aircraft in the same flight phase as of the current time; ZS has the corresponding segmentation point recognition result sequence QF=(QF1, QF2, …, QF v , …, QF w ), where QF v For ZS v Corresponding segmentation point recognition results; QF v =0 or QF v =1; where QF v =0 means ZS v The corresponding ADS-B data is not the flight phase cut-off point; QF v =1 means ZS v The corresponding ADS-B data is the flight phase division point.

[0061] Specifically, each time ADS-B data returned by a target aircraft that is performing a flight mission is received, a corresponding speed-altitude array list ZS is obtained. Here, all arrays in the speed-altitude array list are arrays in the same flight phase state. As an example: if the first target split point has been determined, when determining the second target split point, the corresponding speed-altitude data list is not obtained from the beginning of the flight, but from after the first target split point. That is, at this time, the flight phases corresponding to the speed-altitude arrays contained in the corresponding speed-altitude array list are all cruising phases. If the first target split point has not been determined, the speed-altitude array is obtained from the time of take-off.

[0062] Furthermore, ZS has a corresponding segmentation point recognition result sequence QF, wherein each recognition result in QF is a recognition result of whether the ADS-B data obtained by the HMM model is a target segmentation point, wherein QF v =0 means ZS v The corresponding ADS-B data is not the flight phase cut-off point; QF v =1 means ZS v The corresponding ADS-B data is the flight phase division point.

[0063] S020, input ZS and QF into the HMM model to obtain the prediction results and mark them until two predicted target split points are obtained, and the prediction ends; wherein the prediction result is used to indicate whether an ADS-B data received after the current time is a flight phase split point; ZS is the observation sequence of the HMM model; QF is the hidden sequence of the HMM model.

[0064] Specifically, the above ZS and QF are input 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, the prediction result corresponding to an ADS-B data received after the current time is obtained, and the prediction result here indicates whether an ADS-B data received after the current time is the target segmentation point.

[0065] The HMM model is a hidden Markov model. The HMM model here is trained based on the complete trajectory to be processed that has been completed in several historical times, and each ADS-B data on the trajectory is labeled (whether it is a target cut point). It has corresponding parameters: initial state probability vector, state transition probability matrix and observation probability matrix. The initial state probability vector represents the probability of being in each hidden state at the initial moment, which provides the initial conditions for the subsequent state transition and the generation of observations. The state transition probability matrix describes the probability of transitioning between different hidden states. It depicts the law of hidden state changes over time and is the core of the dynamic change of the HMM model. The observation probability matrix gives the probability of generating each observation under each hidden state. The connection between the hidden state and the observation value is established, so that we can infer the hidden state through the observed data.

[0066] The observation values ​​of the above-mentioned model in the present application include the speed and time in each ADS-B data because in the process of flight climbing or descending, there is a process of climb-level flight-climb-level flight-climb, or a process of descent-level flight-descent-level flight, in which the speed and altitude are mutually constrained. In this embodiment, the HMM model is used as the prediction model because the HMM model has a learning function. In the level flight phase of the climbing or descending phase, since the relationship between its corresponding speed and time is different from that of the cruising phase, the HMM model will not identify the process of climb-level flight-climb-level flight-climb or the process of descent-level flight-descent-level flight in the process of flight climbing or descending as a cruising process, so that the predicted result is more accurate. Therefore, this embodiment uses speed and altitude as the observation sequence of the HMM model, which makes it easier 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. In an exemplary embodiment of the present application, the model may also be input with only ZS to obtain prediction results and mark them until two predicted target segmentation points are obtained, and then the prediction ends.

[0067] In an exemplary embodiment of the present application, after step S010, the method further includes: S030, input ZS and QF into the HMM model. When a target split point is predicted, each time an ADS-B data returned by a target aircraft is received, 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 currently received from the target aircraft when a target segmentation point is predicted; GQ z GQ is the speed-altitude array corresponding to the zth ADS-B data returned by the target aircraft currently received when a target split point is predicted; z =(gv z ,gh z ); gv z For GQ z Corresponding speed; gh z For GQ z The corresponding height.

[0068] S040, according to GQ, the corresponding critical speed-height determinant is obtained .

[0069] S050, if GH is greater than the preset determinant threshold, 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.

[0070] In this embodiment, when a target split point is predicted, the target split point is the split point between the climbing phase and the cruising phase predicted by the HMM model. However, due to various reasons, the ADS-B data of the flight phase may be delayed or due to other reasons. After the target split point is determined, the next target split point is predicted based on the ADS-B data after the target split point as the observation sequence. If the target split point is predicted to be early, that is, it has not yet reached the actual target split point, but the HMM model determines the trajectory point corresponding to the ADS-B data before the actual target split point as the target split point, then the previous part of the subsequent observation state contains part of the speed-altitude data corresponding to the climbing phase, which will make the prediction of the next target split point inaccurate. Therefore, after obtaining the HMM model After the predicted first target split point, it needs to be further verified. The determination method is to obtain whether the speed-altitude array corresponding to each ADS-B between the target split point and 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 split 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 split 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 target split point can be re-determined using dichotomy. The speed-altitude determinant composed of the ADS-B data between the re-determined target split point and the next target split point should be approximately collinear (GH is less than or equal to the preset determinant threshold). Therefore, this embodiment verifies the first target split point determined by the HMM model and makes timely corrections to avoid affecting the accuracy of the second target split point determined by the HMM model.

[0071] Please refer to Figure 2 As shown, an embodiment of the present application provides a flight stage determination device 100, the device comprising: an acquisition unit 110, a noise determination unit 120, an altitude determination unit 130 and a stage division unit 140, wherein: The acquisition unit 110 is used to acquire 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.

[0072] The noise determination unit 120 is used to obtain the data noise Z corresponding to the trajectory to be processed according to S; wherein Z meets the following conditions: ;t n is the time corresponding to the nth ADS-B data; t i 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; h(t i ) is the altitude corresponding to the i-th ADS-B data; is the average window height corresponding to the i-th ADS-B data.

[0073] The altitude determination unit 130 is used to obtain a preset basic cruising altitude if Z is greater than a preset data noise threshold; wherein the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S; and the preset basic cruising altitude is less than the maximum flight altitude corresponding to the flight altitude corresponding to the ADS-B data.

[0074] The stage division unit 140 is used to divide the trajectory to be processed into flight stages according to a preset basic cruising altitude; wherein the flight stages include a climbing stage, a cruising stage and a descending stage.

[0075] Those skilled in the art will appreciate that various aspects of the present application may be implemented as devices, methods or program products. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "device" herein.

[0076] The electronic device according to this embodiment of the present application is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0077] 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 at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different device components (including storage devices and processors).

[0078] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present application.

[0079] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0080] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: operating means, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0081] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0082] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, 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 a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID devices, tape drives, and data backup storage devices, etc.

[0083] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application 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 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 implementation method of the present application.

[0084] 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 the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of the present specification.

[0085] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, device or component, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with 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 thereof.

[0086] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution device, apparatus, or device.

[0087] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0088] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user 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 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., using an Internet service provider to connect through the Internet).

[0089] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0090] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0091] The above are only specific implementations 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 a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for determining a flight phase, characterized in that: The method comprises: S100, obtaining 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, according to S, obtain the data noise Z corresponding to the trajectory to be processed; wherein Z meets the following conditions: ;t n is the time corresponding to the nth ADS-B data; t i 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; h(t i ) is the altitude corresponding to the i-th ADS-B data; is the average window height corresponding to the i-th ADS-B data; S300, if Z is greater than a preset data noise threshold, obtaining a preset basic cruising altitude; wherein the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S; and the preset basic cruising altitude is less than the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data; S400, dividing the trajectory to be processed into flight phases according to a preset basic cruising altitude; wherein the flight phases include a climbing phase, a cruising phase and a descending phase.

2. The flight phase determination method according to claim 1, characterized in that: Step S300 includes: S310, 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 a; S320, obtaining a preset number of flight heights in h in descending order to obtain a key flight height list Gh=(Gh1, Gh2, ..., Gh x , …, Gh y ); x = 1, 2, ..., y; where y is the preset number; Gh x is the xth critical flight altitude; S330, determining MIN(Gh) as a preset basic cruising altitude; wherein MIN() is a preset minimum value determination function.

3. The flight phase determination method according to claim 2, characterized in that: y meets the following characteristics: y=(n / L)×(n / 10000); Wherein, L is the flight distance corresponding to the trajectory to be processed.

4. The flight phase determination method according to claim 3, characterized in that: Step S400 includes: S410, according to S, obtain the height difference list C = (C1, C2, ..., C i , …, C n ), where C i is the i-th height difference; C i =|α×MIN(Gh)-h(t i )|; α is the height adjustment parameter; 0<α<1; S420, according to C, obtain the key height difference list GC = (GC1, GC2, ..., GC c , …, GC d ), c = 1, 2, ..., d, where d is the number of critical height differences; GC c is the cth key height difference; GC c Less than the preset height difference threshold; S430, determining the position corresponding to the earliest time in the GC in the trajectory to be processed as the first division point, and determining the position corresponding to the latest time in the trajectory to be processed as the second division point; S440, dividing the trajectory to be processed according to the first dividing point and the second dividing point, wherein the period from the starting point of the trajectory to be processed to the first dividing point is the climbing phase; the period from the first dividing point to the second dividing point is the cruising phase; and the period from the second dividing point to the end point of the trajectory to be processed is the descending phase.

5. The flight phase determination method according to claim 4, characterized in that: α meets the following conditions: α=α’×β； α' is the basic height adjustment parameter; β is the fluctuation adjustment parameter.

6. The flight phase determination method according to claim 5, characterized in that: β is determined according to the following steps: S001, obtaining the altitude corresponding to each ADS-B data of the historical trajectory in 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, so as to obtain the historical altitude 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 Lh is the altitude list corresponding to the historical trajectory in the cruise phase 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 eth time within the historical time window; e =(Lh e，1 , Lh e，2 ,…,Lh e，r ,…,Lh e，f（e） ); r = 1, 2, ..., f (e); f (e) is Lh e The amount of ADS-B data included; Lh e，r For Lh e The altitude corresponding to the rth ADS-B data included; S002, according to Lh, obtain the historical average fluctuation value corresponding to the trajectory to be processed ; Wherein, avg() is the preset average value determination function; S003, if PB is greater than a preset fluctuation value threshold, determine that β<1, otherwise determine that β>1.

7. A flight phase determination device, characterized in that: The device comprises: The acquisition unit is used to acquire 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; The noise determination unit is used to obtain the data noise Z corresponding to the trajectory to be processed according to S; wherein Z meets the following conditions: ;t n is the time corresponding to the nth ADS-B data; t i 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; h(t i ) is the altitude corresponding to the i-th ADS-B data; is the average window height corresponding to the i-th ADS-B data; an altitude determination unit, configured to obtain a preset basic cruising altitude if Z is greater than a preset data noise threshold; wherein the preset basic cruising altitude is determined according to the flight altitude corresponding to each ADS-B data in S; and the preset basic cruising altitude is less than the maximum flight altitude corresponding to the flight altitudes corresponding to the ADS-B data; The phase division unit is used to divide the trajectory to be processed into flight phases according to a preset basic cruising altitude; wherein the flight phases include a climbing phase, a cruising phase and a descending phase.

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

9. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 8.

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