Method for designing vertical route of airplane based on dynamic planning

Abstract

The invention belongs to the technical field of aircraft performance design, and particularly relates to a method for designing an aircraft vertical route based on dynamic programming, which comprises the following steps: firstly, determining a fixed task by combing the scene of the whole flight task, constructing the general form of the whole vertical route according to the sequence, and decomposing into a plurality of local vertical routes; then, the flight weight of the cruise height is marked, and each flight process is divided into a plurality of stages through the change of the weight; and setting the flight height of the aircraft at the beginning of each stage as a state, and establishing a relationship between the state and the flight stage. And setting the flight height of the aircraft at the beginning of each stage as a state, and establishing a relationship between the state and the flight stage: increasing a state numerical value to climb, not changing the numerical value to cruise flight, and decreasing the numerical value to descend. Therefore, the task process is processed into a multi-stage decision-making problem, no post-effect is obviously satisfied, and the optimization problem of the vertical air route is processed by using a dynamic planning method.

Description

Technical Field

[0001] The present application belongs to the technical field of aircraft performance design, and in particular relates to a method for designing an aircraft vertical route based on dynamic programming. Background Art

[0002] Aircraft is the most efficient and comfortable means of long-distance transportation in the world. The route design of an aircraft affects its flight mission. Reasonable and optimized route design is crucial to reducing the cost of aircraft use.

[0003] Aircraft route design mainly includes two parts: horizontal route design and vertical route design. Horizontal route design refers to the projection of the flight trajectory of the aircraft from the departure airport to the destination airport on the earth plane. Ideally, the aircraft flies along the great circle route of the earth, and the horizontal route is the shortest. Usually, the aircraft flies along the navigation point, and the horizontal route is basically fixed.

[0004] Vertical route design refers to the projection of the flight trajectory of the aircraft from the departure airport to the target airport in the vertical plane, also known as the vertical route. For transport aircraft, vertical route design is to determine the cruising altitude of the aircraft. In the case of a long route distance, the cruising altitude of the aircraft can be directly selected as the optimal cruising altitude. In the case of a short route distance, the cruising altitude of the aircraft needs to be slightly lower than the optimal cruising altitude.

[0005] For design scenarios where some aircraft need to reach medium and low altitudes multiple times during missions, traditional aircraft vertical route design is not fully applicable. First, the traditional vertical route design of transport aircraft is oriented towards high vertical routes, and lacks the mission scenario of lowering the cruising altitude during transport missions; secondly, the traditional vertical route design of combat aircraft often has only one medium and low altitude cruising / combat mission segment, and lacks the mission scenario of lowering the cruising altitude multiple times during missions; finally, the traditional vertical route design of aircraft is more inclined to performance calculation, and lacks the process of vertical route optimization design. Therefore, it is very important to develop an aircraft vertical route optimization design method. Summary of the invention

[0006] The purpose of this application is to provide a method for designing an aircraft vertical route based on dynamic programming, so as to solve the problem that the existing transport aircraft route design lacks a vertical route optimization design process.

[0007] The technical solution of the present application is: a method for designing an aircraft vertical route based on dynamic programming, comprising:

[0008] Determine the aircraft type and the location of the take-off and landing airports, determine the fixed mission segments and conditions of the aircraft; preliminarily construct the entire vertical route according to the sequence of the fixed mission segments;

[0009] The entire vertical route is split according to each fixed mission segment to form several local vertical routes;

[0010] Extract one of the local vertical routes, obtain the status information of the local vertical route during the execution of the task, and divide the status information into multiple stages; make decisions for different stages separately to obtain decision results;

[0011] The state transition equation is used to describe the state relationship of each adjacent stage, and an index function with horizontal flight distance as an index is established according to the state relationship of each adjacent stage and the decision results;

[0012] Obtain the maximum horizontal flight distance of the index function in each adjacent stage to form the optimal strategy and optimal index function for the corresponding local vertical route. The optimal index function is denoted as f k (s k ); and then select the next local vertical route, until all local vertical routes are optimized, the optimization result of the entire vertical route is obtained.

[0013] Preferably, each fixed mission segment includes aircraft mission segment 1 , aircraft mission segment 2 and aircraft mission segment 3 .

[0014] Preferably, the conditions of each fixed task segment are:

[0015] Determine the conditions for the aircraft mission segment 1, the distance from the take-off airport is XX1, the flight altitude is HH1, and the distance the aircraft flies level in mission segment 1 is LL1;

[0016] Determine the conditions for the aircraft mission segment 2, the distance from the take-off airport is XX2, the flight altitude is HH2, and the distance the aircraft flies level in mission segment 2 is LL2;

[0017] Determine the conditions for the aircraft's mission segment 3: the distance from the take-off airport is XX3, the flight altitude is HH3, and the distance the aircraft flies level in mission segment 3 is LL3.

[0018] Preferably, the specific method for constructing the entire vertical route is: preliminarily setting the weight of each flight segment according to the data of the mission segment and the design parameters of the aircraft.

[0019] Preferably, for subsonic jet aircraft, the optimal cruising altitude and cruising speed are evaluated based on the flying weight.

[0020] Preferably, the specific method for splitting the entire vertical route is: every two fixed flight segments and the intermediate variable flight segment constitute a local vertical route, and n fixed flight segments in the air and the take-off and landing segments constitute a total of n+1 local vertical routes.

[0021] Preferably, it is divided into four local vertical routes, from the take-off segment to the mission segment 1 is the first local vertical route; from the mission segment 1 to the mission segment 2 is the second local vertical route; from the mission segment 2 to the mission segment 3 is the third local vertical route; from the mission segment 3 to the landing segment is the fourth local vertical route.

[0022] Preferably, the status information includes the cruising altitude during the mission execution, which is marked as: H 1 , H 2 , H 3 ,…H m , the airport altitude is marked as H 机场 ; The take-off weight of the aircraft during the mission is marked as: G 1 , G 2 , G 3 ,…G n+1 , through the change of weight, the flight process is divided into n stages.

[0023] Preferably, the aircraft weight corresponding to different stages is weight G k , weight G k The corresponding state is s k (1≤k≤n+1); when (1≤k≤n), the aircraft is in take-off state, when k=n+1, the aircraft is in landing state; take-off weight G 1 ~G n The takeoff fuel consumption under the conditions is related to the weight, which can ensure that the G k >G k+1 Under the condition (1≤k≤n-1), the weight G at the end of takeoff k '>G k+1 '.

[0024] Preferably, the method of dividing the state information into multiple stages is: according to the division of cruising altitude and aircraft weight, the state s of the entire endurance process is set. k,j , where 1≤k≤n+1, 1≤j≤m, state s k,j Information included: Flight altitude equals H j , the flight weight is equal to G k,j ; G k,j Is the aircraft in G k is the takeoff weight, the weight when taking off and climbing to the jth altitude, G n+1,j is the landing weight of the aircraft in G n+1 The weight when descending from the jth height; due to the initial climbing weight G k '>G k+1 ', according to the performance data law, the cruising weight G under the same altitude conditions k,j >G k+1,j .

[0025] Preferably, the relationship between each state and the multiple stages is: an increase in the state value indicates climbing, an unchanged value indicates cruising flight, and a decrease in the value indicates descent.

[0026] Preferably, the specific method for making separate decisions at different stages is:

[0027] A set of data is used to mark the cruising altitude during the mission, and a set of weight data is used to mark the weight of the aircraft during the mission. Each flight process is divided into multiple stages through the change in weight; the flight altitude of the aircraft at the beginning of each stage is set as the state, and a relationship is established between the state and the flight stage.

[0028] Preferably, the decision result between the states in adjacent stages is determined as: D(s k,j )={s k+1,1 ,s k+1,2 ,…,s k+1,m}; Combined with the law of the aircraft's vertical route, the decision result is simplified, that is, D(s k,j )={s k+1,j ,s k+1,j+1 ,…,s k+1,m}, and then get the plane from s k,j Run to s k+1,l (j≤l≤m) flight mode.

[0029] Preferably, the aircraft is from s k,j Run to s k+1,l The flight method of (j≤l≤m) is: first, from H j Climb to H l , then from G k,l Cruise to G k+1,l ; The plane from s k,j Run to s n+1,l The flight method of (l≤j≤m) is: first from G k,j Cruise to G n+1,l , then from H j Down to H l .

[0030] Preferably, the state relationship of each adjacent stage is specifically as follows: according to the height and weight information contained in each state, combined with the state design during the entire endurance process, the state relationship of the aircraft from s k,j Run to s k+1,l (j≤l≤m) state transfer equation.

[0031] Preferably, the state transfer equation is:

[0032]

[0033] Among them G climb (Gk , H j ) is the calculation of the aircraft's G k Take off at takeoff weight and climb to H j Weight at height; G decline (G k+1 , H l ) is the calculation of the plane from H l Descend to the airport at G k+1 is the descent weight under the condition of landing weight, and △G is the weight step.

[0034] Preferably, the index function with the horizontal flight distance as the index is:

[0035] L (k,j) =L (k,j) (s k,j ,u k,j ),G climb (G k+1 ,H l )>G decline (G n+1 ,H l )

[0036] L (k,j) =L (k,j) (s k,j ,u k,j )+L decline ,G climb (G k+1 ,H l )≤G decline (G n+1 ,H l )

[0037]

[0038] L k,n =L (k,j),n +L climb,(k,j)

[0039] Among them, L (k,j) (s k,j ,u k,j ) indicates that the j-th height state in the k-th stage is s k,j Take decision k,j The flight distance in stage k;

[0040] L decline,j is the horizontal flight distance when the aircraft descends from the jth altitude to the airspace above the airport;

[0041] L (k,j),n (s k,j , p k,n ) indicates that the j-th height state in the k-th stage is sk,j Adopt strategy p k,n When the flight distance of the latter sub-process is L k,n (s k , p k,n ) indicates that the state at stage k is s k Adopt strategy p k,n When the flight distance of the latter sub-process is L climb,(k,j) Indicates that the initial state is s k The horizontal flight distance when taking off and climbing to the jth altitude.

[0042] Preferably, for a given state s k , the indicator function value changes with the strategy, and different indicator function values ​​can be obtained by adopting different strategies.

[0043] Preferably, the relationship between the optimal index function and the index function is:

[0044]

[0045] Where opt means taking the optimal value, and the terminal condition is f n+1 (s n+1 )=0.

[0046] The method of designing the vertical route of an aircraft based on dynamic programming in the present application divides each flight process into multiple stages through the change of weight. The flight altitude of the aircraft at the beginning of each stage is set as the state, and a relationship is established between the state and the flight segment: an increase in the state value indicates climbing, an unchanged value indicates cruising flight, and a decrease in the value indicates descent. In this way, the mission process is processed into a multi-stage decision-making problem, and the no aftereffect is obviously satisfied, and the dynamic programming method is used to process the optimization problem of the vertical route. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution provided by the present application, the following is a brief introduction to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present application.

[0048] Figure 1 This is a schematic diagram of the overall process of this application;

[0049] Figure 2 This is a schematic diagram of the scenario in which the aircraft in this application performs multiple missions at low and medium altitudes;

[0050] Figure 3 For this application, a schematic diagram of the entire vertical route is initially constructed in a sequential order;

[0051] Figure 4 This is a schematic diagram of the state setting of the vertical route of the aircraft in this application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] A method for designing an aircraft vertical route based on dynamic programming, such as Figure 1 , including the following steps:

[0054] Step S100, determine the aircraft type and the location of the take-off and landing airport, and determine each fixed mission segment and condition of the aircraft.

[0055] In one specific example, the aircraft type is a subsonic jet aircraft.

[0056] Preferably, the departure airport and the landing airport are XX4 apart;

[0057] like Figure 2 The fixed mission segments include aircraft mission segment 1, aircraft mission segment 2 and aircraft mission segment 3.

[0058] The conditions for each fixed task segment are:

[0059] Determine the conditions for the aircraft mission segment 1, the distance from the take-off airport is XX1, the flight altitude is HH1, and the distance the aircraft flies level in mission segment 1 is LL1;

[0060] Determine the conditions for the aircraft mission segment 2, the distance from the take-off airport is XX2, the flight altitude is HH2, and the distance the aircraft flies level in mission segment 2 is LL2;

[0061] Determine the conditions for the aircraft's mission segment 3: the distance from the take-off airport is XX3, the flight altitude is HH3, and the distance the aircraft flies level in mission segment 3 is LL3.

[0062] Step S200, preliminarily constructing the entire vertical route according to the sequence of each fixed task segment.

[0063] like Figure 3 Specifically, the weight of each flight segment is preliminarily set according to the mission segment data and the aircraft design parameters. For subsonic jet aircraft, the optimal cruising altitude and cruising speed can be evaluated based on the flight weight. The optimal cruising altitude is temporarily used to fill the gaps between mission segments.

[0064] Step S300 , splitting the entire vertical route according to each fixed mission segment to form a plurality of local vertical routes.

[0065] The specific method is: every two fixed flight segments and the variable flight segment in the middle constitute a local vertical route, and n fixed flight segments in the air and the take-off and landing segments constitute a total of n+1 local vertical routes.

[0066] It is divided into four local vertical routes: from the take-off segment to mission segment 1 is the first local vertical route; from mission segment 1 to mission segment 2 is the second local vertical route; from mission segment 2 to mission segment 3 is the third local vertical route; from mission segment 3 to landing segment is the fourth local vertical route.

[0067] In this way, the multi-stage decision problem is constructed, and it meets the requirement of no aftereffect. That is, given the state of a certain stage, the development of the process after this stage is not affected by the state of the previous stages. When the state of all stages is determined, the whole process is determined. The dynamic programming method is used to deal with the performance optimization problem of the entire vertical route.

[0068] Step S400, extracting one of the local vertical routes, obtaining the state information of the local vertical route during the task execution, and dividing the state information into multiple stages; making decisions for different stages respectively to obtain decision results.

[0069] like Figure 4 The status information includes the cruising altitude during the mission, which is marked as (H 1 , H 2 , H 3 ,…H m ), the airport altitude is marked as H 机场 .

[0070] The takeoff weight of the aircraft during the mission is marked as (G 1 , G 2 , G 3 ,…G n+1 ), the flight process is divided into n stages through the change of weight.

[0071] The aircraft weight corresponding to different stages is weight G k , weight G k The corresponding state is s k (1≤k≤n+1). When (1≤k≤n), the aircraft is in take-off state, and when k=n+1, the aircraft is in landing state. Take-off weight G 1 ~G n The takeoff fuel consumption under the conditions is related to the weight, which can ensure that the G k >G k+1 Under the condition (1≤k≤n-1), the weight G at the end of takeoff k '>G k+1 '.

[0072] The method of dividing the state information into multiple stages is as follows: according to the division of cruising altitude and aircraft weight, the state s of the entire endurance process is set k,j , where 1≤k≤n+1, 1≤j≤m, state s k,j Information included: Flight altitude equals H j , the flight weight is equal to G k,j . G k,j Is the aircraft in G k is the takeoff weight, the weight when taking off and climbing to the jth altitude, G n+1,j is the landing weight of the aircraft in G n+1 The weight when descending from the jth height. Due to the initial climbing weight G k '>G k+1 ', according to the performance data law, the cruising weight G under the same altitude conditions k,j >G k+1,j .

[0073] The relationship between each state and multiple stages is: the state value increases for climbing, the value remains unchanged for cruising, and the value decreases for descending. In this way, the task process is processed into a multi-stage decision-making problem, and no aftereffect is obviously satisfied. Therefore, the dynamic programming method can be used to deal with the optimization problem of task performance.

[0074] The specific methods for making decisions at different stages are as follows:

[0075] A set of data is used to mark the cruising altitude during the mission, and a set of weight data is used to mark the weight of the aircraft during the mission. Each flight process is divided into multiple stages through the change of weight. The flight altitude of the aircraft at the beginning of each stage is set as the state, and a relationship is established between the state and the flight stage.

[0076] In this way, the task process is processed into a multi-stage decision-making problem. The no aftereffect is obviously satisfied, and the dynamic programming method is used to deal with the optimization problem of the vertical route.

[0077] Then determine the decision results between the states in the adjacent stages, which is D(s k,j )={s k+1,1 ,s k+1,2 ,…,s k+1,m Combined with the rules of the aircraft's vertical route, the decision-making results are simplified, that is, D(s k,j )={s k+1,j ,s k+1,j+1 ,…,s k+1,m}, and then get the plane from s k,j Run to s k+1,l (j≤l≤m) flight mode.

[0078] Aircraft from s k,j Run to sk+1,l The flight method of (j≤l≤m) is: first, from H j Climb to H l , then from G k,l Cruise to G k+1,l ; The plane from s k,j Run to s n+1,l The flight method of (l≤j≤m) is: first from G k,j Cruise to G n+1,l , then from H j Down to H l .

[0079] Step S500, using a state transition equation to describe the state relationship between adjacent stages, and establishing an index function with the horizontal flight distance as an index according to the state relationship between adjacent stages and the decision result.

[0080] The state relationship of each adjacent stage is as follows: Based on the height and weight information contained in each state, combined with the state design during the entire flight process, the aircraft is obtained from s k,j Run to s k+1,l (j≤l≤m) state transfer equation.

[0081] The state transfer equation is:

[0082]

[0083] Among them G climb (G k , H j ) is the calculation of the aircraft's G k Take off at takeoff weight and climb to H j Weight at height. G decline (G k+1 , H l ) is the calculation of the plane from H l Descend to the airport at G k+1 is the descent weight under the condition of landing weight, and △G is the weight step.

[0084] The indicator function is a quantitative indicator to measure the quality of a process. It is a quantitative function defined on the entire process and all subsequent sub-processes.

[0085] The index function with horizontal flight distance as the index is:

[0086] L (k,j) =L (k,j) (s k,j ,u k,j ),G climb (G k+1 ,H l )>G decline (Gn+1 ,H l ) (2)

[0087] L (k,j) =L (k,j) (s k,j ,u k,j )+L decline ,G climb (G k+1 ,H l )≤G decline (G n+1 ,H l ) (3)

[0088]

[0089] L k,n =L (k,j),n +L climb,(k,j) (5)

[0090] Among them, L (k,j) (s k,j ,u k,j ) indicates that the j-th height state in the k-th stage is s k,j Take decision k,j The flight distance in stage k,

[0091] L decline,j It is the horizontal flight distance when the aircraft descends from the jth altitude to the airspace above the airport.

[0092] L (k,j),n (s k,j , p k,n ) indicates that the j-th height state in the k-th stage is s k,j Adopt strategy p k,n When the flight distance of the latter sub-process is L k,n (s k , p k,n ) indicates that the state at stage k is s k Adopt strategy p k,n When , the flight distance of the latter sub-process is,

[0093] L climb,(k,j) Indicates that the initial state is s k The horizontal flight distance when taking off and climbing to the jth altitude.

[0094] For a given state s k , the indicator function value changes with the strategy, and different indicator function values ​​can be obtained by adopting different strategies.

[0095] Step S600, obtaining the maximum horizontal flight distance of the index function in each adjacent stage, forming the optimal strategy and optimal index function corresponding to the local vertical route, the optimal index function is recorded as f k (s k ); and then select the next local vertical route, until all local vertical routes are optimized, the optimization result of the entire vertical route is obtained.

[0096] Preferably, the relationship between the optimal index function and the index function is:

[0097] f k (s k )=opt uk∈Dk(sk) {L climb,(k,j) +L (k,j) +f k+1 (s k+1 )},k=n,n-1,...,1 (6)

[0098] Where opt means taking the optimal value, and the terminal condition is f n+1 (s n+1 )=0.

[0099] The names mentioned in this article are as follows:

[0100] Flight mission scenario - the specific requirements of the flight mission proposed by the customer, such as requiring the aircraft to take off from Airport 1, fly 300km away at an altitude of 5km and Ma0.50 for 50 minutes, then fly 1000km away at an altitude of 3km and Ma0.40 for 40 minutes, and finally land at Airport 2.

[0101] Altitude – the altitude at which the aircraft is flying

[0102] Speed ​​– the speed at which the aircraft is flying

[0103] Flight segment - a specific flight process of an aircraft within a period of time as required. The flight mission segments involved in this article include: take-off segment, climb segment, cruise segment, mission segment, descent segment and landing segment.

[0104] Takeoff phase - the process of an aircraft taking off from the airport and reaching the initial climb.

[0105] Climb phase - the process by which an aircraft increases its altitude.

[0106] Cruise phase - the process of an aircraft flying long distances, during which the altitude and speed changes are small.

[0107] Mission segment - the specific requirements for the aircraft to perform the flight mission, generally at a given position, altitude, speed, and a certain level of flight, and can also perform material collection and release operations.

[0108] Descent phase - the process of an aircraft decreasing its altitude.

[0109] Endurance phase - the process of an aircraft continuously flying in the air, including the climb phase, cruise phase, mission phase, descent phase, etc.

[0110] Landing phase - the process from the end of the aircraft's descent to the touchdown.

[0111] Fixed flight segments - In the flight mission scenario, some flight segments are fixed, including take-off, landing and various mission segments.

[0112] Variable flight segments - In the flight mission scenario, some flight segments are variable, including the climb segment, cruise segment and descent segment. Variable flight segments are the object of vertical route optimization design.

[0113] Vertical route refers to the projection of the flight trajectory of the aircraft in the vertical plane from the departure airport to the target airport. The vertical route is the continuation of the customer's flight mission.

[0114] Local vertical route refers to a vertical route consisting of two adjacent fixed flight segments and the variable flight segment in between. n fixed flight segments and take-off and landing segments together constitute n+1 local vertical routes, and all local vertical routes constitute the entire vertical route.

[0115] Dynamic programming, a branch of operations research, is a mathematical method for solving the optimization of multi-stage decision-making processes. The terminology of dynamic programming in this article is quoted from "Optimization Theory and Algorithms (2nd Edition)" edited by Chen Baolin, Tsinghua University Press, 2nd Edition, October 2005

[0116] Stage - a term in dynamic programming, is a natural division of the entire vertical route. This article divides the stages based on the aircraft takeoff weight.

[0117] State - A term in dynamic programming. In the whole process, the natural state or objective conditions at the beginning of each stage are called states.

[0118] Decision-making is a term in dynamic programming. After the state of a stage is determined, different choices can be made to evolve to a certain state in the next stage.

[0119] Strategy - In the terminology of dynamic programming, a sequence of decisions is called a strategy. 1 Starting from the decision u at each stage k (s k The sequence consisting of )(k=1,2,...,n) is called the whole process strategy, the inspection strategy, generally denoted as p 1,n (s 1 ).

[0120] State transfer equation - a term in dynamic programming, if the state s at stage k k and decision u k Given, then the state s of the k+1th stage k+1 Depending on the situation, k+1 With s k ,u k There is a functional relationship between them, denoted as s k+1 =T(s k ,u k ), this relationship is called the state transfer equation.

[0121] Index function - a term in dynamic programming. The index function is a quantitative indicator to measure the quality of a process. It is a quantitative function defined on the entire process and all subsequent sub-processes.

[0122] Optimal strategy - a term in dynamic programming, making the indicator function V k,n (s k ,p k,n ) The strategy p that achieves the optimal value k,n ※ It is called the optimal strategy in the k-th posterior subprocess, making the indicator function V 1,n (s 1 ,p 1,n ) The strategy p that achieves the optimal value 1,n ※ It is called the optimal strategy in the whole process, or simply the optimal strategy.

[0123] Finally, it should be noted that: the drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for designing an aircraft vertical route based on dynamic programming, characterized in that: include: Determine the aircraft type and the location of the take-off and landing airports, determine the fixed mission segments and conditions of the aircraft; preliminarily construct the entire vertical route according to the sequence of the fixed mission segments; The entire vertical route is split according to each fixed mission segment to form several local vertical routes; Extract one of the local vertical routes, obtain the status information of the local vertical route during the execution of the task, and divide the status information into multiple stages; make decisions for different stages separately to obtain decision results; The state transition equation is used to describe the state relationship of each adjacent stage, and an index function with horizontal flight distance as an index is established according to the state relationship of each adjacent stage and the decision results; Obtain the maximum horizontal flight distance of the index function in each adjacent stage to form the optimal strategy and optimal index function for the corresponding local vertical route. The optimal index function is denoted as f k (s k ); and then select the next local vertical route, until all local vertical routes are optimized, the optimization result of the entire vertical route is obtained.

2. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The fixed mission segments include aircraft mission segment 1, aircraft mission segment 2, and aircraft mission segment 3.

3. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 2, characterized in that: The conditions for each fixed task segment are: Determine the conditions for the aircraft mission segment 1, the distance from the take-off airport is XX1, the flight altitude is HH1, and the distance the aircraft flies level in mission segment 1 is LL1; Determine the conditions for the aircraft mission segment 2, the distance from the take-off airport is XX2, the flight altitude is HH2, and the distance the aircraft flies level in mission segment 2 is LL2; Determine the conditions for the aircraft's mission segment 3: the distance from the take-off airport is XX3, the flight altitude is HH3, and the distance the aircraft flies level in mission segment 3 is LL3.

4. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The specific method of constructing the entire vertical route is: based on the data of the mission segment and the design parameters of the aircraft, the weight of each flight segment is preliminarily set.

5. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 4, characterized in that: For subsonic jets, the optimum cruising altitude and cruising speed are evaluated based on the flying weight.

6. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The specific method for splitting the entire vertical route is: every two fixed flight segments and the variable flight segment in the middle constitute a local vertical route, and n fixed flight segments in the air and the take-off and landing segments constitute a total of n+1 local vertical routes.

7. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 6, characterized in that: It is divided into four local vertical routes: from the take-off segment to mission segment 1 is the first local vertical route; from mission segment 1 to mission segment 2 is the second local vertical route; from mission segment 2 to mission segment 3 is the third local vertical route; from mission segment 3 to landing segment is the fourth local vertical route.

8. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: Status information includes the cruising altitude during the mission, marked as: H1, H2, H3, ... H m , the airport altitude is marked as H 机场 ; The take-off weight of the aircraft during the mission is marked as: G1, G2, G3, ...G n+1 , through the change of weight, the flight process is divided into n stages.

9. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 8, characterized in that: The aircraft weight corresponding to different stages is weight G k , weight G k The corresponding state is s k (1≤k≤n+1); when (1≤k≤n), the aircraft is in take-off state, when k=n+1, the aircraft is in landing state; take-off weight G1~G n The takeoff fuel consumption under the conditions is related to the weight, which can ensure that the G k >G k+1 Under the condition (1≤k≤n-1), the weight G at the end of takeoff k '>G k+1 '.

10. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 9, characterized in that: The method of dividing the state information into multiple stages is as follows: according to the division of cruising altitude and aircraft weight, the state s of the entire endurance process is set k,j , where 1≤k≤n+1, 1≤j≤m, state s k,j Information included: Flight altitude equals H j , the flight weight is equal to G k,j ; G k,j Is the aircraft in G k is the takeoff weight, the weight when taking off and climbing to the jth altitude, G n+1,j is the landing weight of the aircraft in G n+1 The weight when descending from the jth height; due to the initial climbing weight G k '>G k+1 ', according to the performance data law, the cruising weight G under the same altitude conditions k,j >G k+1,j .

11. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The relationship between each state and multiple stages is: an increase in state value indicates climbing, an unchanged value indicates cruising flight, and a decrease in value indicates descent.

12. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The specific methods for making decisions at different stages are as follows: A set of data is used to mark the cruising altitude during the mission, and a set of weight data is used to mark the weight of the aircraft during the mission. Each flight process is divided into multiple stages through the change in weight; the flight altitude of the aircraft at the beginning of each stage is set as the state, and a relationship is established between the state and the flight stage.

13. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 10, characterized in that: Determine the decision results between the states in adjacent stages: D(s k,j )={s k+1,1 ,s k+1,2 ,…,s k+1,m }; Combined with the law of the aircraft's vertical route, the decision result is simplified, that is, D(s k,j )={s k+1,j ,s k+1,j+1 ,…,s k+1,m }, and then get the plane from s k,j Run to s k+1,l (j≤l≤m) flight mode.

14. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 13, characterized in that: Aircraft from s k,j Run to s k+1,l The flight method of (j≤l≤m) is: first, from H j Climb to H l , then from G k,l Cruise to G k+1,l ; The plane from s k,j Run to s n+1,l The flight method of (l≤j≤m) is: first from G k,j Cruise to G n+1,l , then from H j Down to H l .

15. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The state relationship of each adjacent stage is as follows: Based on the height and weight information contained in each state, combined with the state design during the entire flight process, the aircraft is obtained from s k,j Run to s k+1,l (j≤l≤m) state transition equation.

16. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 1, characterized in that: The state transfer equation is: Among them G climb (G k , H j ) is the calculation of the aircraft's G k Take off at takeoff weight and climb to H j Weight at height; G decline (G k+1 , H l ) is the calculation of the plane from H l Descend to the airport at G k+1 is the descent weight under the condition of landing weight, and △G is the weight step.

17. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 16, characterized in that: The index function with horizontal flight distance as the index is: L (k,j) =L (k,j) (s k,j ,u k,j ),G climb (G k+1 ,H l )>G decline (G n+1 ,H l ) L (k,j) =L (k,j) (s k,j ,u k,j )+L decline ,G climb (G k+1 ,H l )≤G decline (G n+1 ,H l ) L k,n =L (k,j),n +L climb,(k,j) Among them, L (k,j) (s k,j ,u k,j ) indicates that the j-th height state in the k-th stage is s k,j Take decision k,j The flight distance in stage k; L decline,j is the horizontal flight distance when the aircraft descends from the jth altitude to the airspace above the airport; L (k,j),n (s k,j , p k,n ) indicates that the j-th height state in the k-th stage is s k,j Adopt strategy p k,n When the flight distance of the latter sub-process is L k,n (s k , p k,n ) indicates that the state at stage k is s k Adopt strategy p k,n When the flight distance of the latter sub-process is L climb,(k,j) Indicates that the initial state is s k The horizontal flight distance when taking off and climbing to the jth altitude.

18. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 17, characterized in that: For a given state s k , the indicator function value changes with the strategy, and different indicator function values ​​can be obtained by adopting different strategies.

19. The method for designing an aircraft vertical route based on dynamic programming as claimed in claim 17, characterized in that: The relationship between the optimal index function and the index function is: f k (s k )=opt uk∈Dk(sk) {L climb,(k,j) +L (k,j) +f k+1 (s k+1 )},k=n,n-1,...,1 Where opt means taking the optimal value, and the terminal condition is f n+1 (s n+1 )=0.

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