Flight plan optimization method considering conflict resolution

Abstract

The application discloses a flight plan optimization method considering conflict resolution, belongs to the field of unmanned aerial vehicle multi-aircraft conflict resolution, and obtains flight parameters of a current flight scene; the flight parameters are input into a flight plan optimization model established in advance, wherein the flight plan optimization model takes actual total flight quantity, average delay time and flight adjustment quantity as an objective function, and takes operation limitation conditions as constraints; the flight plan optimization model is solved by using a simulated annealing genetic algorithm to obtain an optimal flight plan of the current flight scene. Conflict is relieved through multi-aircraft flight plan optimization, one or more flight plan schemes are obtained, continuity and smoothness of air traffic flow are ensured, and operation efficiency of urban air traffic is improved.

Description

Technical Field

[0001] This invention relates to the field of multi-aircraft conflict resolution technology for unmanned aerial vehicles (UAVs), and particularly to a flight plan optimization method that takes conflict resolution into account. Background Technology

[0002] Because multiple aircraft flying in the same airspace may share routes, intersect routes, and share takeoff and landing sites, the distance between them may be less than the prescribed interval. If a collision occurs between aircraft, it will affect flight safety. Therefore, when formulating flight plans for multiple aircraft, it is necessary to avoid flight conflicts between them, increase safety margins, and efficiently complete the flight tasks of each aircraft to avoid delays and improve economic efficiency.

[0003] Currently, existing flight planning schemes only consider fuel saving and do not take into account other factors in the planning. Moreover, most studies are based on civil aviation operations and large and medium-sized passenger aircraft, while there are very few research results applicable to general aviation operations and unmanned aerial vehicle systems. Summary of the Invention

[0004] This invention provides a flight plan optimization method that considers conflict resolution, in order to address the problem that when multiple aircraft flying in the same airspace share routes, cross routes, and share take-off and landing sites, the distance between multiple aircraft may be less than the prescribed interval, affecting flight safety.

[0005] This invention provides a flight plan optimization method considering conflict resolution, comprising the following steps: obtaining flight parameters for the current flight scenario; inputting the flight parameters into a pre-established flight plan optimization model, wherein the flight plan optimization model uses the actual total number of flights, average delay time, and flight adjustment amount as objective functions, and operational constraints as constraints; and solving the flight plan optimization model using a simulated annealing genetic algorithm to obtain the optimal flight plan for the current flight scenario.

[0006] In one embodiment of the present invention, the actual total number of flights I in the objective function of the flight plan optimization model is:

[0007]

[0008]

[0009] Where g is the aircraft serial number, ξ is the number of aircraft, h is the current flight serial number of the aircraft, η is the number of scheduled flights for the aircraft on that day, and c g,h For aircraft e g The status of the h-th flight;

[0010] Average delay time T d for:

[0011]

[0012] Where, d g,h For aircraft e g Delay time for flight h;

[0013] The aircraft e is calculated by the time of a single flight and the time interval between two adjacent flights. g The planned number of flights for the day is η:

[0014]

[0015] Among them, L g For aircraft e g The length of the flight path, where χ is the interference level coefficient. `round()` and `round-down()` are the floor function and the round-to-floor function, respectively. The numerator expression is the aircraft e. g The total flight time for the day, expressed as the denominator is aircraft e g The round trip time, T f The end time of the last flight of the day, T s v is the start time of the first flight of the day. g For aircraft e g The running speed, T D To complete the minimum time interval after the outbound flight, T R The minimum time interval after completing the return flight;

[0016] Flight adjustment volume H is:

[0017]

[0018] Where h' is the actual sequence number corresponding to the h-th flight;

[0019] The objective function, consisting of the total number of actual flights, average delay time, and flight adjustment amount, is as follows:

[0020] minW=-ω1I+ω2T d +ω3H

[0021] Wherein, ω1, ω2, and ω3 are the weighting coefficients for the actual total number of flights, the average delay time, and the flight adjustment amount, respectively, and ω1+ω2+ω3=1.

[0022] In one embodiment of the present invention, the operational constraints of the flight plan optimization model include:

[0023] (1) The time interval between two consecutive flights of the same aircraft is greater than or equal to the preset interval duration ΔT:

[0024] (DEP g,h+1 +d g,h+1 )-(ARR g,h +d g,h )≥ΔT

[0025]

[0026] Among them, DEP g,h and ARR g,h aircraft e g The departure and arrival times of the h-th flight, where ΔT is the preset interval duration, T D and T R T represents the minimum time interval after completing the outbound flight and the return flight, respectively. D <T R mod() is the modulo function, and f is the total number of flights that all aircraft can provide;

[0027] (2) The arrival time of each aircraft after completing its last flight back to the origin shall not be later than the preset time point T. f :

[0028] ARR g,η +d g,η ≤T f

[0029] (3) The same airspace grid cell cannot be occupied by two or more aircraft within the same time period:

[0030]

[0031] Among them, T in (e g ,f h ,(i,j,k)) and T out (e g ,f h (i,j,k) represent aircraft e g The times when the h-th flight enters and leaves the airspace grid cell (i,j,k) are T in (e g' ,f h' ,(i,j,k)) and T out (e g' ,f h' (i,j,k) represent aircraft e g' The times when the h'th flight enters and leaves the airspace grid cell (i,j,k), f h This represents the status of flight h. The status is 0 when canceled and 1 when in flight. g and v g' aircraft eg and e g' The running speed;

[0032] (4) The arrival time of each aircraft at each waypoint is within the preset range:

[0033]

[0034] in, For aircraft e g At the time when p passes the t-th waypoint g,t For aircraft e g The t-th waypoint passed along the route, where Δd is the maximum allowed delay time;

[0035] (5) Each aircraft selects only one flight path for any given flight:

[0036]

[0037]

[0038] Among them, l s For aircraft e g Current flight path, L g For aircraft e g The set of all flight paths, s for aircraft e g The current flight track number, σ is L g The number of tracks within the set.

[0039] In one embodiment of the present invention, the flight plan optimization model is solved using a simulated annealing genetic algorithm to obtain the optimal flight plan for the current flight scenario, including:

[0040] Step 1: If aircraft e g If the h-th flight does not conflict with the original flight schedule, then it will proceed to the h+1th flight; if aircraft e g If the h-th flight conflicts with the original flight schedule, the departure time DEP will be adjusted. g,h and entry time ARR g,h Delay ΔT a Otherwise, proceed to step 2.

[0041] Step 2: Let o+1 be the adjustment quantity, o = 0, 1, 2, ..., η-h; Delay the exit time DEP g,h+o and entry time ARR g,h+o Then the departure time is DEP g,h DEP g,h+1 ... DEP g,h+o and entry time ARR g,h ARR g,h+1... ARR g,h+o All were delayed, if aircraft e g If the h-th flight does not conflict with the original flight schedule, then proceed to the h+1-th flight; otherwise, o = o+1, and continue repeating step 2 until o = η-h; if the conflict still exists when o = η-h, then proceed to step 3.

[0042] Step 3: Cancel aircraft e g The hth flight; return to step 1 and check aircraft e g The h+1th flight.

[0043] The flight plan optimization method considering conflict resolution in this invention establishes a flight plan optimization model based on low-altitude environment risk assessment and eVTOL aircraft trajectory planning. The model uses the actual total number of flights, average delay time, and flight adjustment amount as objective functions and operational constraints as constraints. An improved simulated annealing genetic algorithm is used to solve for the optimal flight plan, ensuring the continuity and smoothness of air traffic flow and improving the operational efficiency of urban air traffic.

[0044] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0045] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 A flowchart illustrating a flight plan optimization method considering conflict resolution according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the framework of a flight plan optimization method considering conflict resolution according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the flight plan before optimization, provided according to an embodiment of the present invention.

[0049] Figure 4 This is a schematic diagram of an optimized flight plan provided according to an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram comparing flight plans before and after optimization according to an embodiment of the present invention;

[0051] Figure 6 This is a schematic diagram of the objective function with different combinations of weight coefficients according to an embodiment of the present invention;

[0052] Figure 7This is a schematic diagram comparing the average delay time before and after optimization when ζ=120, according to an embodiment of the present invention.

[0053] Figure 8 This is a schematic diagram comparing the average delay time before and after optimization when ζ=180, according to an embodiment of the present invention.

[0054] Figure 9 This is a schematic diagram comparing the average delay time before and after optimization when ζ=240, according to an embodiment of the present invention.

[0055] Figure 10 v provided according to embodiments of the present invention g A diagram showing the comparison of average delay time before and after optimization at a speed of 30 m / s;

[0056] Figure 11 v provided according to embodiments of the present invention g A diagram comparing the average delay time before and after optimization at a speed of 60 m / s.

[0057] Figure 12 v provided according to embodiments of the present invention g A diagram showing the comparison of average delay time before and after optimization when the speed is 90m / s. Detailed Implementation

[0058] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0059] In an embodiment of the present invention, under a pre-defined operating environment, the eVTOL aircraft performs a flight mission in a low-altitude urban environment according to a planned flight path. The flight plan optimization method considering conflict resolution makes the following assumptions:

[0060] (1) Optimize multi-aircraft flight plans within the existing urban low-altitude airspace environment and based on the optimal flight path.

[0061] (2) Starting point P1, ending point P n The location is known.

[0062] (3) All scenarios involving flight conflicts, such as shared routes, intersecting routes, and shared take-off and landing sites, are taken into account.

[0063] (4) The eVTOL aircraft has a constant flight speed and known performance parameters, and there are no other assigned tasks during the journey.

[0064] (5) Meteorological conditions and control conditions are suitable for operation, without considering extreme weather conditions or air traffic management restrictions.

[0065] Considering the practicalities of urban air traffic operations, the parameters used in the embodiments of the present invention are shown in Table 1.

[0066] Table 1 Model Variable Table

[0067]

[0068] Figure 1 This is a flowchart of a flight plan optimization method that takes into account conflict resolution, according to an embodiment of the present invention.

[0069] like Figure 1 As shown, this flight plan optimization method considering conflict resolution includes the following steps:

[0070] In step S101, the flight parameters of the current flight scenario are obtained.

[0071] To find the optimal flight plan for the current flight, we first obtain the parameters of multiple aircraft in the flight scenario. The specific parameters are applied in the following examples and will not be listed here.

[0072] In step S102, flight parameters are input into a pre-established flight plan optimization model, wherein the flight plan optimization model takes the total number of actual flights, average delay time and flight adjustment amount as objective functions and the operational constraints as constraints.

[0073] The initial mission assignments for multiple aircraft are independent. In order to resolve potential conflicts between aircraft with minimal adjustments, it is necessary to establish a multi-aircraft flight plan optimization model from a global perspective.

[0074] In embodiments of the present invention, such as Figure 2 As shown, in the flight plan optimization model, the objective function includes the actual total number of flights, the average delay time, and the flight adjustment amount. The actual total number of flights is:

[0075]

[0076]

[0077] Where g is the aircraft serial number, ξ is the number of aircraft, h is the current flight serial number of the aircraft, η is the number of scheduled flights for the aircraft on that day, and c g,h For aircraft e g The status of the h-th flight.

[0078] The time between a single flight and the time interval between two adjacent flights can be used to calculate the aircraft's e gPlanned number of flights for the day:

[0079]

[0080] Among them, L g For aircraft e g The length of the flight path, where χ is the interference level coefficient. `round()` and `round-down()` are the floor function and the round-to-floor function, respectively. The numerator expression is the aircraft e. g The total flight time for the day is expressed as the denominator for aircraft e. g The time required for a round trip.

[0081] Since the total number of flights is a positive integer, different optimization schemes may have the same result, making it impossible to assess their quality. Therefore, a new standard needs to be added. Thus, the average delay time is:

[0082]

[0083] Where, d g,h For aircraft e g The delay time of flight h.

[0084] There are discrepancies between the actual flight operations and the plan. To ensure smooth operations, adjustments to the original flight plan should be minimized. Therefore, the flight adjustment amount is:

[0085]

[0086] Where h' is the actual sequence number corresponding to the h-th flight.

[0087] In summary, the objective function of the flight plan optimization model is:

[0088] minW=-ω1I+ω2T d +ω3H

[0089] Wherein, ω1, ω2, and ω3 are the weighting coefficients for the actual total number of flights, the average delay time, and the flight adjustment amount, respectively, and ω1+ω2+ω3=1.

[0090] In embodiments of the present invention, the operational constraints of the flight plan optimization model include: flight interval constraints, end time constraints, flight conflict constraints, transit time constraints, and unique track constraints.

[0091] Specifically, a certain time interval is required between two consecutive flights of the same aircraft. Therefore, the time interval between two consecutive flights of the same aircraft must be greater than or equal to the preset interval length ΔT.

[0092] (DEP g,h+1 +d g,h+1 )-(ARRg,h +d g,h )≥ΔT

[0093]

[0094] Among them, DEP g,h and ARR g,h aircraft e g The departure and arrival times of the h-th flight, where ΔT is the preset interval duration, T D and T R T represents the minimum time interval after completing the outbound flight and the return flight, respectively. D <T R mod() is the modulo function, and f is the total number of flights that all aircraft can provide.

[0095] The arrival time of each aircraft returning to its origin after completing its final flight should not be later than T. f :

[0096] ARR g,η +d g,η ≤T f

[0097] An airspace grid is a unit for checking flight conflicts between aircraft. To ensure flight safety, the same grid cannot be occupied by two or more aircraft at the same time.

[0098]

[0099] Among them, T in (e g ,f h ,(i,j,k)) and T out (e g ,f h (i,j,k) represent aircraft e g At the time of the h-th flight entering and leaving the grid (i,j,k), T in (e g' ,f h' ,(i,j,k)) and T out (e g' ,f h' (i,j,k) represent aircraft e g' The times when the h'th flight enters and leaves the grid (i,j,k) are f h This represents the status of flight h. The status is 0 when canceled and 1 when in flight. g and v g' aircraft e g and e g' The running speed.

[0100] Using interpolation, the coordinates and corresponding times of the waypoints can be calculated:

[0101] p g,t =(x g,t ,y g,t ,z g,t )

[0102]

[0103] Where, p g,t For aircraft e g The t-th waypoint passed along the way, For aircraft e g The time when the t-th waypoint is reached.

[0104] During actual flight, the arrival times of aircraft at various waypoints must be within a certain range:

[0105]

[0106] Where Δd is the maximum allowable delay time.

[0107] Each aircraft can only choose one flight path for any given flight:

[0108]

[0109]

[0110] Among them, l s For aircraft e g Current flight path, L g For aircraft e g The set of all flight paths, s for aircraft e g The current flight track number, σ is L g The number of tracks within the set.

[0111] In step S103, the simulated annealing genetic algorithm is used to solve the flight plan optimization model to obtain the optimal flight plan for the current flight scenario.

[0112] The sheer number of aircraft providing urban air traffic services, coupled with the excessive complexity of considering flight conflicts for all aircraft, necessitates decomposing the multi-aircraft conflict process into smaller-scale problems. Given the periodicity of urban air traffic services, all routes and aircraft have the same priority and can be arbitrarily ordered, but the resulting sequences vary significantly. Each time, two aircraft are assigned, considering the airspace grid shared by both routes. If a conflict exists, one flight will be delayed. Multi-aircraft conflicts are resolved through all possible combinations of the two aircraft, identifying the optimal sequence among all possible combinations that maximizes the total number of flights for all aircraft and minimizes the average delay time.

[0113] Based on the above approach, a solution strategy is designed for delaying the current flight, delaying the current and subsequent flights, and canceling the current flight. The simulated annealing genetic algorithm is then used to solve the flight plan optimization model to obtain the optimal flight plan for the current flight scenario. The specific details are as follows:

[0114] Step 1: If aircraft e g If the h-th flight does not conflict with the original flight schedule, then it will proceed to the h+1th flight; if aircraft e g If the h-th flight conflicts with the original flight schedule, the departure time DEP will be adjusted. g,h and entry time ARR g,h Delay ΔT a Otherwise, proceed to Step 2.

[0115] Step 2: Let o+1 be the adjustment quantity, o = 0, 1, 2, ..., η-h; Delay the exit time DEP g,h+o and entry time ARR g,h+o Then the departure time is DEP g,h DEP g,h+1 ... DEP g,h+o and entry time ARR g,h ARR g,h+1 ... ARR g,h+o All were delayed, at which point if aircraft e g If the h-th flight does not conflict with the original flight schedule, then proceed to the h+1-th flight; otherwise, o = o+1, and continue to repeat the process until o = η-h; if the conflict still exists when o = η-h, then proceed to Step 3.

[0116] Step 3: Cancel aircraft e g The hth flight; return to Step 1 and check aircraft e g The h+1th flight.

[0117] This solution strategy utilizes delayed aircraft e gFor flight h, the departure and arrival times will be conflicted. If the conflict persists, the departure and arrival times of subsequent flights will be delayed to avoid conflict for flight h. If Step 1 and Step 2 are invalid, flight h will be cancelled. The pseudocode for the entire process is shown in Table 2.

[0118] Table 2. Pseudocode for the solution strategy

[0119]

[0120] Due to aircraft e g Flight h was delayed, its departure time changed, and a re-examination of conflicts between this flight and its preceding flight was required. Flight h+1 will begin when there is no conflict between flight h and its preceding flight.

[0121] The flight plan optimization method considering conflict resolution of the present invention will be described below through specific embodiments.

[0122] Set the parameters as shown in Table 3.

[0123] Table 3 Parameter Settings

[0124]

[0125]

[0126] Before flight plan optimization, the total number of planned flights for the day was 120, the actual number of flights was 109, the number of canceled flights was 11, the number of delayed flights was 20, the total delay time was 2401 seconds, and the average delay time per flight was 120.05 seconds. Specific details of each flight's delay are shown in Table 4 and... Figure 3 As shown; after flight plan optimization, the total number of planned flights for the day was 120, the actual number of flights was 117, the number of canceled flights was 3, the number of delayed flights was 20, the total delay time was 1911 seconds, and the average delay time per flight was 95.55 seconds. Specific information on the delays of each flight is shown in Table 5 and... Figure 4 As shown in Table 6, the optimized flight plan increased the actual number of flights by 6.67% and reduced the average delay time by 20.66%. Figure 5 As shown.

[0127] Due to the settings for airspace grid division precision and aircraft operating speed, aircraft occupy a grid for a relatively short period of time. Most delayed flights only need to delay their original departure time by 1-2 minutes to operate normally. However, it is necessary to check again whether the adjusted flight conflicts with other flights. This is why flights ranked lower in the order have longer delays and more cancellations.

[0128] Table 4 Flight Plans Before Optimization

[0129]

[0130] Table 5 Optimized Flight Plan

[0131]

[0132] Table 6 Comparison of Flight Plans Before and After Optimization

[0133]

[0134] To compare the impact of different weighting coefficients, total planned flights, and operating speed on flight plan optimization, this invention optimizes multi-aircraft flight plans under multiple different numerical settings and compares the results.

[0135] (1) Weighting coefficients. Under the condition of the same planned flight volume, different combinations of weighting coefficients were set and the flight plan optimization results were analyzed. ω1, ω2, ω3∈[0.1,0.8], Δω=0.1, and ω1+ω2+ω3=1. The results are as follows: Figure 6 As shown in the figure, it can be seen that the smaller the diameter of the point, the smaller the objective function value; among them, ω1=0.4, ω2=0.4, ω3=0.2 is the optimal combination of weight coefficients, at which the objective function value is minimized.

[0136] (2) Total Planned Flights. Under the condition of the same number of aircraft and planned flights, different total planned flights were set, and the flight plan optimization results were analyzed. The results for ζ = 120, 180, and 240 are as follows: Figures 7-9 As shown in the figure. Calculations show that when ζ = 120, the average delay times for each flight before and after optimization are 120.05s and 95.25s, respectively, a reduction of 20.66%; when ζ = 180, the average delay times for each flight before and after optimization are 113.81s and 92.06s, respectively, a reduction of 19.10%; and when ζ = 240, the average delay times for each flight before and after optimization are 125.12s and 103.83s, respectively, a reduction of 17.01%. It can be seen that when the total number of planned flights is small, the actual total number of flights for all aircraft is large, and the average delay time for each flight is short. With the increase in the total number of planned flights, theoretically the total number of flights for all aircraft should increase, but due to the increased airspace congestion, the actual total number of flights for all aircraft does not change significantly, while the average delay time for each flight increases.

[0137] (3) Operating Speed. Under the condition of the same number of aircraft and planned flights, different operating speeds were set, and the flight plan optimization results were analyzed. g The results at speeds of 30 m / s, 60 m / s, and 90 m / s are as follows: Figures 10-12As shown. After calculation, v g At a speed of 30 m / s, the average delay times for each flight before and after optimization were 162.4 s and 136.3 s, respectively, a reduction of 16.07%; g At a speed of 60 m / s, the average delay times for each flight before and after optimization were 120.05 s and 95.25 s, respectively, a reduction of 20.66%; g At a speed of 90 m / s, the average delay times for each flight before and after optimization were 97.5 s and 83.00 s, respectively, a reduction of 14.87%. It can be seen that at slower operating speeds, aircraft occupy a single airspace grid for a longer period, resulting in less room for flight adjustments and a longer average delay time for each flight. As operating speed increases, the time an aircraft occupies a single airspace grid decreases, the room for flight adjustments increases, and the average delay time for each flight decreases.

[0138] According to the flight plan optimization method considering conflict resolution proposed in the embodiments of the present invention, based on low-altitude environment risk assessment and eVTOL aircraft trajectory planning, a flight plan optimization model is established with the actual total number of flights, average delay time and flight adjustment amount as objective functions and operational constraints as constraints. An improved simulated annealing genetic algorithm is used to solve the optimal flight plan, which ensures the continuity and smoothness of air traffic flow and improves the operational efficiency of urban air traffic.

[0139] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0140] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0141] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

Claims

1. A flight plan optimization method considering conflict resolution, characterized in that, The method comprises the following steps: obtaining flight parameters of a current flight scene; inputting the flight parameters into a pre-established flight plan optimization model, wherein the flight plan optimization model takes actual total flights, average delay time and flight adjustment amount as an objective function, and takes operation limit conditions as constraints; solving the flight plan optimization model by using a simulated annealing genetic algorithm to obtain an optimal flight plan of the current flight scene; in the objective function of the flight plan optimization model, the actual total flights I is: wherein g is the aircraft number, ξ is the number of aircraft, h is the current flight number of the aircraft, η is the planned number of flights of the aircraft for the day, c g,h for the aircraft e g the status of the hth flight; Average delay time T d is: wherein d g,h for the aircraft e g delay time of the hth flight; The aircraft e is calculated by the single flight time and the time interval between two adjacent flights g The amount of scheduled flights for the day is η: Among them, L g For aircraft e g The length of the flight path, where χ is the interference level coefficient. `round()` and `round-down()` are the floor function and the round-to-floor function, respectively. The numerator expression is the aircraft e. g The total flight time for the day, expressed as the denominator is aircraft e g The round trip time, T f The end time of the last flight of the day, T s v is the start time of the first flight of the day. g For aircraft e g The running speed, T D To complete the minimum time interval after the outbound flight, T R The minimum time interval after completing the return flight; the flight adjustment amount H is: wherein h' is an actual serial number corresponding to the hth flight; the objective function composed of the actual total flights, the average delay time and the flight adjustment amount is: minW = -ω1I + ω2T d +ω3H wherein ω1, ω2 and ω3 are weight coefficients of the actual total flights, the average delay time and the flight adjustment amount respectively, and ω1+ω2+ω3=1; the operation limit conditions of the flight plan optimization model comprise: (1) a time interval between adjacent two flights of the same aircraft is greater than or equal to a preset interval duration ΔT: (DEP g,h+1 +d g,h+1 )-(ARR g,h +d g,h )≥ΔT where DEP g,h and ARR g,h are the departure time and arrival time of the e g th flight of the aircraft, respectively, ΔT is a preset interval, T D and T R are the minimum time intervals after the outbound flight and the return flight, respectively, T D <T R , mod() is a modulo function, and f is the total flight capacity provided by all aircraft. (2) The arrival time of each aircraft returning to the starting point after completing the last flight is not later than the preset time point T f : ARR g,η +d g,η ≤T f (3) the same airspace grid unit cannot be jointly occupied by two or more aircrafts in the same time period: Among them, T in (e g ,f h ,(i,j,k)) and T out (e g ,f h (i,j,k) represent aircraft e g The times when the h-th flight enters and leaves the airspace grid cell (i,j,k) are T in (e g' ,f h' ,(i,j,k)) and T out (e g' ,f h' (i,j,k) represent aircraft e g' The times when the h'th flight enters and leaves the airspace grid cell (i,j,k), f h This represents the status of flight h. The status is 0 when canceled and 1 when in flight. g and v g' aircraft e g and e g' The running speed; (4) an arrival time of each aircraft at each track point is within a preset range: wherein for the aircraft e g the time of passing the tth waypoint, p g,t for the aircraft e g the tth waypoint passed, Δd is the longest delay time allowed; (5) only one track is selected for any flight of each aircraft: wherein, l s for an aircraft e g current flight leg, L g for an aircraft e g set of all flight legs, s for an aircraft e g sequence number of the current flight leg, σ for L g number of legs in the set.

2. The method of claim 1, wherein, solving the flight plan optimization model by using the simulated annealing genetic algorithm to obtain the optimal flight plan of the current flight scene, comprising: Step 1 : If the hth flight of aircraft e g has no conflict with the original schedule, go to the (h+1)th flight; if the hth flight of aircraft e g has a conflict with the original schedule, delay the departure time DEP g,h and the arrival time ARR g,h by ΔT a ; otherwise, go to Step 2; Step 2: Let o+1 be the adjustment number, o = 0, 1, 2, …, η-h; delay the departure time DEP g,h+o and the arrival time ARR g,h+o , then the departure time DEP g,h , DEP g,h+1 , …, DEP g,h+o and the arrival time ARR g,h , ARR g,h+1 , …, ARR g,h+o are delayed, if the hth flight of the aircraft e g has no conflict with the original flight, then enter the (h+1)th flight; otherwise, o = o+1, continue to repeat Step 2 until o = η-h; if the conflict still exists when o = η-h, then enter Step 3; Step 3: cancel the hth flight of aircraft e g ; return to Step 1 to check the (h+l)th flight of aircraft e g .

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