A method and apparatus for on-time arrival planning of flight routes based on particle swarm optimization algorithm

Abstract

This invention discloses a method and apparatus for on-time arrival planning of flight routes based on particle swarm optimization (PSO). The method includes: planning flight routes based on flight platform performance to generate preset flight segments; constructing a mathematical model based on the preset flight segments; pre-setting the PSO algorithm based on the mathematical model to generate a planning PSO algorithm; calculating the mathematical model based on the planning PSO algorithm to generate a planned speed; and completing the on-time arrival planning of the flight route based on the planned speed. The mathematical model in this invention fills a theoretical gap in on-time arrival functionality, and this invention fills the gap in the application of swarm intelligence algorithms in on-time arrival functionality by quickly and accurately solving the mathematical model.

Description

Technical Field

[0001] This invention relates to the field of flight route planning, and more specifically, to a method and apparatus for on-time arrival planning of flight routes based on particle swarm optimization algorithm. Background Technology

[0002] In the field of flight path planning within mission planning, on-time arrival capability refers to the function of planning the flight mission of an aircraft platform to ensure it arrives at a designated waypoint on time. Given the characteristics of aircraft—high flight speed, limited flight performance, high time accuracy requirements, and the inability to easily change waypoints along the flight path—the planning algorithm for achieving on-time arrival capability demands high precision and high speed. While there is considerable research on particle swarm optimization (PSO) algorithms, the selection of key parameters (such as particle velocity) varies depending on the application scenario. PSO algorithms have not yet been applied to on-time arrival planning, creating a theoretical gap in the application of PSO algorithms for on-time arrival planning in China.

[0003] Therefore, one or more methods are needed to solve the above problems.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for on-time arrival planning of flight routes based on particle swarm optimization, thereby overcoming one or more problems caused by the limitations and defects of related technologies to a certain extent.

[0006] In a first aspect, the present invention provides a method for on-time arrival planning of flight routes based on particle swarm optimization, comprising the following steps:

[0007] Flight routes are planned based on flight platform performance, preset flight segments are generated, and mathematical models are constructed based on the preset flight segments;

[0008] Based on the mathematical model, the particle swarm algorithm is preset to generate a planning particle swarm algorithm;

[0009] The mathematical model is calculated based on the planning particle swarm algorithm to generate the planned speed, and the flight route is planned to arrive on time based on the planned speed.

[0010] The on-time arrival planning method for flight routes based on particle swarm optimization also includes the following steps:

[0011] Based on the performance of the flight platform, the flight route is planned to generate the first on-time arrival point and the second on-time arrival point;

[0012] The first on-time arrival point is preset as the end point of the planned flight segment, and the planned flight segment is preset as the on-time arrival segment;

[0013] The first on-time arrival point is preset as the start point of the preset flight segment, and the second on-time arrival point is preset as the end point of the preset flight segment, wherein the preset flight segment is a cruise segment.

[0014] The on-time arrival planning method for flight routes based on particle swarm optimization also includes the following steps:

[0015] When the time required to travel from the first on-time arrival point to the second on-time arrival point is greater than or equal to a preset time, an acceleration point is inserted after the first on-time arrival point. Based on the acceleration point, an acceleration mathematical model min|t is constructed. + (v0,v obj ,w0)+t c (v obj ,w + )-(t sp -t st )|;

[0016] Among them, t + (v0,v obj w0) is used to accelerate the time consumption, t c (v obj ,w + ) is the preset acceleration segment true speed cruise time, t sp The exact arrival time at the end point of the flight segment, t st The exact arrival time at the starting point of the flight segment;

[0017] Based on the aforementioned acceleration mathematical model, the acceleration fitness value f is established. + (x i (t))=t + (v0,x i (t),w0)+t c (x i (t),w + )-(t sp -t st );

[0018] Among them, f + (x i (t) represents the acceleration fitness value, t + (v0,x i (t), w0) represents the preset acceleration time for the acceleration segment, t c (x i (t),w + ) is the preset acceleration segment true speed cruise time, t sp The exact arrival time at the end point of the flight segment, tst The exact arrival time at the starting point of the flight segment;

[0019] Based on the aforementioned acceleration mathematical model, constraints for the acceleration mathematical model are established. Where st is the constraint condition, f + (v0,v obj (w0) to accelerate fuel consumption, f c (v obj ,d c ,w + ) is the fuel consumption for true-speed cruise at the preset acceleration distance, f max To carry fuel to the flight platform's starting point, d + (v0,v obj w0) represents the acceleration distance, d c d is the true speed cruising distance, v0 is the total distance of the segment, v0 is the initial speed, v +obj To accelerate the target speed, v max The maximum speed of the flight platform is w0, and the total weight of the flight platform when it begins cruise is w. + The total weight of the flight platform when it begins to accelerate;

[0020] When the time required to travel from the first on-time arrival point to the second on-time arrival point is less than a preset time, a deceleration point is inserted after the first on-time arrival point. Based on the deceleration point, a deceleration mathematical model min|t is constructed. . (v0,v obj ,w0)+t c (v obj ,w . )-(t sp -t st )|;wherein, t . (v0,v obj w0) represents the deceleration time, t c (v obj ,w . ) represents the preset deceleration segment's true speed cruise time, t sp The exact arrival time at the end point of the flight segment, t st Arrival time at the starting point of the flight segment on time;

[0021] Based on the aforementioned deceleration mathematical model, a deceleration fitness value f is established. _ (x i (t))=t _ (v0,x i (t),w0)+t c (x i (t),w . )-(t sp -t st ); where f _(x i (t) represents the deceleration fitness value, t _ (v0,x i (t), w0) represents the preset deceleration time for the deceleration segment, t c (x i (t),w . ) represents the preset deceleration segment's true speed cruise time, t s( The exact arrival time at the end point of the flight segment, t st Arrival time at the starting point of the flight segment on time;

[0022] Based on the aforementioned deceleration mathematical model, the constraints of the deceleration mathematical model are established. Where st is the constraint condition, f . (v0,v obj (w0) represents deceleration fuel consumption, f c (v obj ,d c ,w . () is the fuel consumption for true-speed cruise at the preset deceleration distance, f max To carry fuel to the flight platform's starting point, d . (v0,v obj w0) represents the deceleration distance, d c d is the true speed cruising distance, v0 is the total distance of the segment, v0 is the initial speed, v .obj To decelerate the target speed, v min The minimum speed of the flight platform is w0, and the total weight of the flight platform when it begins cruise is w. _ This is the total weight of the flight platform when it begins to decelerate.

[0023] The on-time arrival planning method for flight routes based on particle swarm optimization also includes the following steps:

[0024] Based on the mathematical model, the particle velocity function of the particle swarm algorithm is preset to generate the preset velocity function.

[0025] Based on the mathematical model, the particle position function of the particle swarm algorithm is preset, and the preset position function is generated.

[0026] Based on the preset velocity function and preset position function, a planning particle swarm algorithm is generated.

[0027] The on-time arrival planning method for flight routes based on particle swarm optimization also includes the following steps:

[0028] Based on the planning particle swarm algorithm, the deceleration mathematical model is calculated. When the globally optimal deceleration particle and the current deceleration particle are on the same side of the preset ideal deceleration, the current deceleration particle is slowed down.

[0029] Based on the aforementioned particle swarm optimization algorithm, the deceleration mathematical model is calculated. When the globally optimal decelerating particle and the current decelerating particle are on opposite sides of the preset ideal deceleration, the decelerating particle velocity is set. Accelerate the currently decelerating particle; where v i－ To decelerate the particle velocity, gbest(t) is the globally preset optimal position, x i (t) represents the preset position, f － (x i (t) represents the fitness of the preset position deceleration, and f(gbest(t)) represents the fitness of the global optimal solution;

[0030] Based on the planning particle swarm algorithm, the acceleration mathematical model is calculated. When the globally optimal accelerating particle and the current accelerating particle are on the same side of the preset ideal acceleration, the current accelerating particle is slowed down.

[0031] Based on the aforementioned particle swarm optimization algorithm, the acceleration mathematical model is calculated. When the globally optimal accelerating particle and the current accelerating particle are on opposite sides of the preset ideal acceleration, the acceleration particle velocity is set. Accelerate the currently accelerated particle; where v i+ To accelerate particle velocity, gbest(t) is the globally preset optimal position, x i (t) represents the preset position, f + (x i f(t)) represents the fitness of the preset position acceleration, and f(gbest(t)) represents the fitness of the global optimal solution.

[0032] The on-time arrival planning method for flight routes based on particle swarm optimization also includes the following steps:

[0033] Based on the aforementioned planning particle swarm algorithm, when the globally optimal particle converges to a preset ideal velocity and reaches a preset terminal position, the mathematical model is calculated to generate the terminal velocity;

[0034] When the end velocity is greater than the ideal velocity, the preset end velocity value is decreased; when the end velocity is less than the ideal velocity, the preset end velocity value is increased.

[0035] When the terminal velocity equals the ideal velocity, the planning particle swarm algorithm completes the calculation of the mathematical model, realizing the timely arrival of the flight route as planned.

[0036] The on-time arrival planning method for flight routes based on particle swarm optimization also includes the following steps:

[0037] When the current decelerating particle is less than the preset minimum speed of the flight platform, a signal is sent to the flight platform that the current route cannot be completed on time.

[0038] When the current accelerating particle is greater than the preset maximum speed of the flight platform, a signal is sent to the flight platform that the current route cannot be completed on time.

[0039] Secondly, the present invention provides a flight path on-time arrival planning device based on particle swarm optimization algorithm, comprising:

[0040] The mathematical model building module is used to plan preset flight segments based on flight platform performance.

[0041] A particle swarm optimization (PSO) algorithm planning module is used to pre-set the PSO algorithm based on the mathematical model.

[0042] The calculation module is used to perform calculations on the mathematical model based on the planning particle swarm algorithm to complete the planning of the flight route to arrive on time.

[0043] Thirdly, the present invention provides an electronic device, comprising:

[0044] A processor and a memory, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the method according to any one of the preceding claims.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of the preceding claims.

[0046] An exemplary embodiment of the present invention provides a method for on-time arrival planning of flight routes based on particle swarm optimization (PSO). The method includes: planning flight routes based on flight platform performance to generate preset flight segments; constructing a mathematical model based on the preset flight segments; pre-setting the PSO algorithm based on the mathematical model to generate a planning PSO algorithm; calculating the mathematical model based on the planning PSO algorithm to generate a planned speed; and completing the on-time arrival planning of the flight route based on the planned speed. This mathematical model fills a theoretical gap in on-time arrival functionality. The present invention fills the gap in the application of swarm intelligence algorithms for on-time arrival functionality by rapidly and accurately solving this mathematical model.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0048] The above and other features and advantages of the present invention will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0049] Figure 1This is a flowchart of a flight route on-time arrival planning method based on particle swarm optimization algorithm, which is an exemplary embodiment of the present invention.

[0050] Figure 2 This is a schematic flowchart of a flight route on-time arrival planning method based on particle swarm optimization algorithm, which is an exemplary embodiment of the present invention.

[0051] Figure 3 This is a schematic block diagram of a flight path on-time arrival planning device based on particle swarm optimization algorithm, which is an exemplary embodiment of the present invention.

[0052] Figure 4 This is a block diagram of an electronic device according to an exemplary embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0055] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the invention can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0056] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0057] In this example embodiment, a flight path on-time arrival planning method based on particle swarm optimization algorithm is first provided; refer to Figure 1 As shown, this method for on-time arrival planning of flight routes based on particle swarm optimization may include the following steps:

[0058] Step S110: Plan the flight route based on the performance of the flight platform, generate a preset flight segment, and construct a mathematical model based on the preset flight segment;

[0059] Step S120: Based on the mathematical model, the particle swarm algorithm is preset to generate a planning particle swarm algorithm;

[0060] Step S130: Calculate the mathematical model based on the planning particle swarm algorithm, generate the planned speed, and complete the planning of the flight route to arrive on time based on the planned speed.

[0061] An exemplary embodiment of the present invention provides a method for on-time arrival planning of flight routes based on particle swarm optimization (PSO). The method includes: planning flight routes based on flight platform performance to generate preset flight segments; constructing a mathematical model based on the preset flight segments; pre-setting the PSO algorithm based on the mathematical model to generate a planning PSO algorithm; calculating the mathematical model based on the planning PSO algorithm to generate a planned speed; and completing the on-time arrival planning of the flight route based on the planned speed. This mathematical model fills a theoretical gap in on-time arrival functionality. The present invention fills the gap in the application of swarm intelligence algorithms for on-time arrival functionality by rapidly and accurately solving this mathematical model.

[0062] The following will further explain a flight path on-time arrival planning method based on particle swarm optimization in this example embodiment.

[0063] In the template configuration step S110, the flight route can be planned based on the flight platform performance, a preset flight segment can be generated, and a mathematical model can be constructed based on the preset flight segment.

[0064] In this example embodiment, the flight route is planned based on the flight platform performance to generate a first on-time arrival point and a second on-time arrival point; the first on-time arrival point is preset as the end point of the planned flight segment, and the planned flight segment is preset as an on-time arrival segment; the first on-time arrival point is preset as the start point of the preset flight segment, and the second on-time arrival point is preset as the end point of the preset flight segment, and the preset flight segment is a cruise segment (excluding segments involving acceleration / deceleration, climb / descent).

[0065] That is, when there are two or more on-time arrival points on a flight route, the on-time arrival points are numbered sequentially from the first on-time arrival point to the nth on-time arrival point, where n is a natural number. The set of on-time arrival points is set as {P}. iLet i = 1, 2, 3, ..., n, where i ranges from 1 to n, and the calculation proceeds sequentially in units of a preset flight segment. At this point, planning for the first on-time arrival point P1 is straightforward; simply adjust the departure time based on the on-time arrival time according to the original route plan. Afterward, arbitrarily choose k ∈ N ∩ [1, n-1], where k ranges from 1 to n-1. If P... k It is the first on-time arrival point that has completed the on-time arrival plan, P. k+1 That is the second punctual arrival point.

[0066] In this example embodiment, when the task time required to travel from the first on-time arrival point to the second on-time arrival point is greater than or equal to a preset time, an acceleration point is inserted after the first on-time arrival point, and an acceleration mathematical model is constructed based on the acceleration point. When the task time required to travel from the first on-time arrival point to the second on-time arrival point is less than the preset time, a deceleration point is inserted after the first on-time arrival point, and a deceleration mathematical model is constructed based on the deceleration point.

[0067] Before establishing the mathematical model, the general assumptions are set as follows: the wind speed and temperature of the flight route are constant, the flight drag coefficient is determined as a fixed constant based on the flight load scheme and the performance of the flight platform, and the cruise is a constant true speed cruise.

[0068] Accelerating mathematical model building: Assuming P k+1 The segment ending at a certain point is a cruising segment. The mathematical model for accelerating this segment (Model 1) is: min|t + (v0,v obj ,w0)+t c (v obj ,w + )-(t sp -t st )|, where t + (v0,v obj w0) is used to accelerate the time consumption, t c (v obj ,w + ) is the preset acceleration segment true speed cruise time, t sp The exact arrival time at the end point of the flight segment, t st The time of arrival at the starting point of the flight segment is the actual time. That is, the sum of the acceleration time and the cruising time of this flight segment (actual time) minus the difference between the actual time of arrival at the end point of the flight segment and the actual time of arrival at the starting point of the flight segment (ideal time). The smaller the difference, the more ideal it is. Therefore, we need to find the minimum difference.

[0069] Simultaneously, based on the aforementioned accelerated mathematical model, constraints for the accelerated mathematical model are established.

[0070]

[0071] Where st is the constraint condition, f + (v0,v obj (w0) to accelerate fuel consumption, f c (v obj ,d c ,w + ) is the fuel consumption for true-speed cruise at the preset acceleration distance, f max To carry fuel to the flight platform's starting point, d + (v0,v obj w0) represents the acceleration distance, d c d is the true speed cruising distance, v0 is the total distance of the segment, v0 is the initial speed, v +obj To accelerate the target speed, v max The maximum speed of the flight platform is w0, and the total weight of the flight platform when it begins cruise is w. + This is the total weight of the flight platform when it begins to accelerate.

[0072] That is, by constraining fuel consumption: the total fuel consumption of acceleration and constant speed cruise cannot exceed the amount of fuel carried by the flight platform at the start of the flight segment.

[0073] Flight distance is constrained: the flight distance for acceleration and true-speed cruise maneuvers is equal to the total distance of the flight segment.

[0074] Constraints are placed on the acceleration target speed: the acceleration target speed is between the initial speed and the maximum flight speed of the flight platform.

[0075] Constraints are placed on the sign of the distance: the distance cannot be negative, the total distance of the flight segment is the actual known distance, and the total distance of the flight segment is non-negative; based on the establishment of the mathematical model of the flight platform acceleration, the acceleration distance is non-negative, so the true speed cruise distance is constrained to make the true speed cruise distance non-negative.

[0076] To facilitate the calculation of the acceleration mathematical model using the particle swarm optimization algorithm, an acceleration fitness value is established. Let f be the acceleration fitness value. + (x i (t))=t + (v0,x i (t),w0)+t c (x i (t),w + )-(t sp -t st ); where f + (x i (t) represents the acceleration fitness value, t + (v0,x i (t), w0) represents the preset acceleration time for the acceleration segment, t c (xi (t),w + ) is the preset acceleration segment true speed cruise time, t sp The exact arrival time at the end point of the flight segment, t st The on-time arrival time of the segment's starting point. That is, the acceleration fitness value is equal to the sum of the acceleration time and the true speed cruise time of this segment (actual time) minus the difference between the on-time arrival time of the segment's ending point and the on-time arrival time of the segment's starting point (ideal time).

[0077] Modeling of the deceleration mathematical model: Assume P k+1 The segment ending at a certain point is a cruising segment, and the mathematical model for deceleration (Model 2) is: min|t _ (v0,v obj ,w0)+t c (v obj ,w _ )-(t sp -t st )|,t _ (v0,v obj w0) represents the deceleration time, t c (v obj ,w _ ) represents the preset deceleration segment's true speed cruise time, t sp The exact arrival time at the end point of the flight segment, t st The on-time arrival time of the segment's starting point. That is, the difference between the sum of the deceleration time and the cruising time at true speed (actual time) of this segment and the difference between the on-time arrival time of the segment's ending point and the on-time arrival time of the segment's starting point (ideal time). The smaller the difference, the more ideal it is, so we need to find the minimum difference.

[0078] Simultaneously, based on the aforementioned deceleration mathematical model, constraints for the deceleration mathematical model are established.

[0079]

[0080] st represents the constraint condition, f _ (v0,v obj (w0) represents deceleration fuel consumption, f c (v obj ,d c ,w _ () is the fuel consumption for true-speed cruise at the preset deceleration distance, f max Carry fuel at the launch point of the flight platform. _ (v0,v obj w0) represents the deceleration distance, d c d is the true speed cruising distance, v0 is the total distance of the segment, v0 is the initial speed, v _obj To decelerate the target speed, v minThe minimum speed of the flight platform is w0, and the total weight of the flight platform when it begins cruise is w. _ This is the total weight of the flight platform when it begins to decelerate.

[0081] That is, by constraining fuel consumption: the total fuel consumption of deceleration and true speed cruise cannot exceed the amount of fuel carried by the flight platform at the start of the flight segment.

[0082] Flight distance is constrained: the flight distance for deceleration and true-speed cruise maneuvers is equal to the total distance of the flight segment.

[0083] Constraints are imposed on the deceleration target speed: the deceleration target speed is between the initial speed and the minimum flight speed of the flight platform.

[0084] Constraints are placed on the sign of the distance: the distance cannot be negative, the total distance of the flight segment is the actual known distance, and the total distance of the flight segment is non-negative; based on the establishment of the mathematical model of the flight platform deceleration, the deceleration distance is non-negative, so the cruise distance is constrained to make the cruise distance non-negative.

[0085] To facilitate the calculation of the deceleration mathematical model using the particle swarm optimization algorithm, a deceleration fitness value is established. Let f be the deceleration fitness value. - (x i (t))=t - (v0,x i (t),w0)+t c (x i (t),w - )-(t sp -t st ), where f - (x i (t) represents the deceleration fitness value, t - (v0,x i (t), w0) represents the preset deceleration time for the deceleration segment, t c (x i (t),w - ) represents the preset deceleration segment's true speed cruise time, t sp The exact arrival time at the end point of the flight segment, t st The on-time arrival time of the segment's starting point. That is, the deceleration fitness value is equal to the sum of the deceleration time and the true speed cruise time of this segment (actual time) minus the difference between the on-time arrival time of the segment's ending point and the on-time arrival time of the segment's starting point (ideal time).

[0086] In this example embodiment, based on the mathematical model, the particle velocity function of the particle swarm algorithm is preset to generate a preset velocity function; based on the mathematical model, the particle position function of the particle swarm algorithm is preset to generate a preset position function; based on the preset velocity function and the preset position function, a planned particle swarm algorithm is generated.

[0087]

[0088]

[0089] Where i = 1, 2, 3, ..., NP, j = 1, 2, 3, ..., D.

[0090] The classic particle swarm optimization algorithm first establishes the execution conditions for the iterative loop (input: number of iterations T; population size NP; problem dimension D; output: global optimal position vector x). * (t). The initial value of the iteration is set to: t = 1); the position vector and velocity vector of the initial particle are set; the fitness value is set so that the fitness value is not less than the preset value and the number of iterations does not exceed the preset iteration limit (in Algorithm 1, steps 1-4).

[0091] Then, based on the value of the current particle i in the population size range of 1 to NP, the optimal position (local optimal position) of each particle is obtained by calculating, comparing and updating the fitness of each particle. Then, the optimal positions of all particles are compared to obtain the global optimal position (steps 5-8 in Algorithm 1).

[0092] The specific implementation involves taking the value of the current particle i in the population size range of 1 to NP, and the value of the problem dimension j in the range of 1 to D. The next velocity of the current particle i is updated based on its previous velocity. Then based on the updated speed Calculate the current position The local optimum position is obtained. Finally, the iteration is completed by assigning a value to t to obtain the global optimum position. (In Algorithm 1, steps 9-16).

[0093] This example improves upon the existing classic particle swarm optimization algorithm by modifying the velocity function. and position function The algorithm was updated to Algorithm 2, the planning particle swarm algorithm.

[0094]

[0095]

[0096] Where i = 1, 2, 3, ..., NP, the problem dimension is one-dimensional, and the parameter D can be ignored.

[0097] The particle swarm optimization algorithm first establishes the execution conditions for the iterative loop (input: number of iterations T; population size NP; terminal search speed v). s >0; Problem dimension D=1.

[0098] Output: Global optimal position vector x * (t)). Then, the initial value for the iteration is set to t = 1, and the particle's position vector is initialized to x. i Finally, the planning particle swarm algorithm initializes the particle position vector by taking a random value between the minimum speed and the maximum speed of the flight platform. The random value follows an average distribution (steps 1-3 in Algorithm 2).

[0099] Based on the value of the current particle i between population size 1 and NP, the position vector of the initialized particle is then verified by substituting it into the model constraints (steps 4-5 in Algorithm 2).

[0100] After verification, the initial velocity of each particle was set to: v i Meanwhile, the execution conditions of the iteration loop are set so that the fitness value is not less than the preset value and the number of iterations does not exceed the preset iteration limit (in Algorithm 2, steps 6-8).

[0101] When the calculation begins, based on the value of the current particle i in the population size range of 1 to NP, the optimal position (local optimal position) of each particle is obtained by calculating, comparing and updating the fitness of each particle. Then, the optimal positions of all particles are compared to obtain the global optimal position (steps 9-12 in Algorithm 2).

[0102] In the embodiments of this example, as Figure 2 As shown, based on the planning particle swarm optimization algorithm, the deceleration mathematical model is calculated. When the globally optimal decelerating particle and the current decelerating particle are on the same side of the preset ideal deceleration, the current decelerating particle is slowed down. Based on the planning particle swarm optimization algorithm, the deceleration mathematical model is calculated. When the globally optimal decelerating particle and the current decelerating particle are on opposite sides of the preset ideal deceleration, the decelerating particle velocity is set. (Its velocity is a multiple of the ratio between the global optimal position and the current position, where the ratio is the absolute value of the particle's current fitness relative to the difference between the absolute value of the particle's current fitness and the absolute value of the global optimal fitness.) Where, v i+ To accelerate particle velocity, gbest(t) is the globally preset optimal position, x i (t) represents the preset position, f + (xi f(gbest(t)) represents the preset position acceleration fitness, and f(gbest(t)) represents the global optimal solution fitness. This accelerates the current decelerating particle.

[0103] Based on the planned particle swarm optimization algorithm, the acceleration mathematical model is calculated. When the globally optimal accelerating particle and the current accelerating particle are on the same side of the preset ideal acceleration, the current accelerating particle is slowed down. Based on the planned particle swarm optimization algorithm, the acceleration mathematical model is calculated. When the globally optimal accelerating particle and the current accelerating particle are on opposite sides of the preset ideal acceleration, the acceleration particle speed is set.

[0104] Its velocity is a multiple of the ratio between the global optimal position and the current position. This ratio is the absolute value of the particle's current fitness, representing the percentage of the difference between the absolute value of the particle's current fitness and the absolute value of the global optimal fitness. Where, v i+ To accelerate particle velocity, gbest(t) is the globally preset optimal position, x i (t) represents the preset position, f + (x i f(x) represents the preset position acceleration fitness, and f(gbest(t)) represents the global optimal solution fitness. The current accelerated particle is accelerated. That is, when f(x)... i When f(gbest(t))≥0, the current particle and the particle corresponding to the global optimal solution fall on the same side of the ideal solution (on the real number axis). (Equality is used to ensure comprehensive classification; a value equal to zero does not affect the algorithm result and will exit in the next loop, returning to the ideal solution). At this time, the current particle x... i To "catch up" with the globally optimal solution in order to find the ideal solution earlier, its speed is: v i =1.5×(gbest(t)-x) i (t)).

[0105] When f(x) i When f(gbest(t)) < 0, the current particle and the global optimal solution fall on different sides of the ideal solution, and the current particle x i The speed depends on "appropriately" finding an ideal solution between itself and the globally optimal solution. (Here, when using the deceleration mathematical model, the settings are...) When using accelerated mathematical models, set Then the particle position is updated. The updated particle position is the sum of the original particle position and the particle velocity (in Algorithm 2, steps 20-24).

[0106] In this example embodiment, based on the planning particle swarm algorithm, when the globally optimal particle converges to the preset ideal velocity and reaches the preset terminal position, the mathematical model is calculated to generate the terminal velocity; when the terminal velocity is greater than the ideal velocity, the preset value of the terminal velocity is decreased; when the terminal velocity is less than the ideal velocity, the preset value of the terminal velocity is increased; when the terminal velocity is equal to the ideal velocity, the calculation of the mathematical model by the planning particle swarm algorithm is completed, and the flight path arrives at the planned destination on time.

[0107] The preset values ​​ensure that the global optimal solution of the particle swarm optimization algorithm moves towards the ideal solution (making f(x(t))). * x(t) = 0 * The convergence process also has a relatively fast speed at the end. The global optimal solution does not need to search for the ideal solution in a "large span", but only needs to search for the ideal solution in a more "careful and subtle" way.

[0108] That is, the end-search process, f(x) i A value greater than 0 indicates that the speed is too fast, and the global optimal solution gbest(t) needs to be reduced, with its speed set to a preset speed v. s The opposite number of f(x); i A value less than 0 indicates that the speed is too slow, and the global optimal solution gbest(t) needs to be increased. Its speed is taken as the preset speed v. s It itself. Each correction can take the preset value v. s = 5km / h (in Algorithm 2, steps 14-18).

[0109] In the embodiments of this example, as Figure 2 As shown, when the current decelerating particle is less than the preset minimum speed of the flight platform, the particle position is corrected by touching the boundary and bouncing back, and at the same time a signal is sent to the flight platform that the current route cannot be completed on time; when the current accelerating particle is greater than the preset maximum speed of the flight platform, the particle position is corrected by touching the boundary and bouncing back, and at the same time a signal is sent to the flight platform that the current route cannot be completed on time (steps 26-29 in Algorithm 2).

[0110] It should be noted that although the steps of the method in this invention are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0111] Furthermore, in this example embodiment, a flight path on-time arrival planning device based on particle swarm optimization algorithm is also provided. (Refer to...) Figure 3As shown, the on-time arrival planning device 400 based on particle swarm optimization algorithm may include: a mathematical model construction module 410, a planning particle swarm optimization algorithm module 420, and a calculation module 430. Wherein:

[0112] Mathematical model building module 410 is used to plan preset flight segments based on flight platform performance;

[0113] The particle swarm optimization algorithm module 420 is used to preset the particle swarm optimization algorithm based on the mathematical model.

[0114] The calculation module 430 is used to perform calculations on the mathematical model based on the planning particle swarm algorithm to complete the planning of the flight route to arrive on time.

[0115] The specific details of each of the above-mentioned particle swarm optimization (PSO)-based flight route on-time arrival planning device modules have been described in detail in the corresponding particle swarm optimization-based flight route on-time arrival planning method, so they will not be repeated here.

[0116] It should be noted that although several modules or units for a flight path on-time arrival planning device 400 based on particle swarm optimization algorithm are mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0117] Furthermore, in an exemplary embodiment of the present invention, an electronic device capable of implementing the above-described method is also provided.

[0118] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented as entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as “circuit,” “module,” or “system.”

[0119] The following reference Figure 4 To describe an electronic device 500 according to such an embodiment of the present invention. Figure 4 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0120] like Figure 4As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0121] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform actions such as... Figure 1 Steps S110 to S130 are shown in the figure.

[0122] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.

[0123] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5203, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0124] Bus 550 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0125] Electronic device 500 can also communicate with one or more external devices 570 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via an input / output (I / O) interface. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 550. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] Through the description of the above embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions of the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of the present invention.

[0127] In exemplary embodiments of the present invention, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the present invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above.

[0128] refer to Figure 5 As shown, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

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

[0130] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0132] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0133] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0134] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0135] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for on-time arrival planning of flight routes based on particle swarm optimization, characterized in that, Includes the following steps: Flight routes are planned based on flight platform performance, preset flight segments are generated, and mathematical models are constructed based on the preset flight segments; Based on the mathematical model, the particle swarm algorithm is preset to generate a planning particle swarm algorithm; The mathematical model is calculated based on the planning particle swarm algorithm to generate the planned speed, and the flight route is planned to arrive on time based on the planned speed. It also includes: planning flight routes based on flight platform performance to generate a first on-time arrival point and a second on-time arrival point; When the task time required to reach the second on-time arrival point from the first on-time arrival point is greater than or equal to the preset time, an acceleration point is inserted after the first on-time arrival point. Based on the acceleration point, an acceleration mathematical model is constructed: min|t+(v0,vobj,w0)+tc(vobj,w+)-(tsp-tst)|. Where t+(v0,vobj,w0) is the acceleration time, tc(vobj,w+) is the preset acceleration segment true speed cruise time, tsp is the time of arrival at the segment end point, and tst is the time of arrival at the segment start point. When the time required to travel from the first on-time arrival point to the second on-time arrival point is less than a preset time, a deceleration point is inserted after the first on-time arrival point. Based on the deceleration point, a deceleration mathematical model is constructed: min|t-(v0,vobj,w0)+tc(vobj,w-)-(tsp-tst)|; where t-(v0,vobj,w0) is the deceleration time, tc(vobj,w-) is the preset deceleration segment true speed cruise time, tsp is the on-time arrival time of the segment termination point, and tst is the on-time arrival time of the segment start point. It also includes the following steps: Based on the mathematical model, the particle velocity function of the particle swarm algorithm is preset to generate the preset velocity function. Based on the mathematical model, the particle position function of the particle swarm algorithm is preset, and the preset position function is generated. Based on the preset velocity function and preset position function, a planning particle swarm algorithm is generated.

2. The flight path on-time arrival planning method based on particle swarm optimization as described in claim 1, characterized in that, It also includes the following steps: The first on-time arrival point is preset as the end point of the planned flight segment, and the planned flight segment is preset as the on-time arrival segment; The first on-time arrival point is preset as the start point of the preset flight segment, and the second on-time arrival point is preset as the end point of the preset flight segment, wherein the preset flight segment is a cruise segment.

3. The flight path on-time arrival planning method based on particle swarm optimization algorithm as described in claim 2, characterized in that, It also includes the following steps: Based on the aforementioned acceleration mathematical model, the acceleration fitness value is set as f+(xi(t))=t+(v0,xi(t),w0)+tc(xi(t),w+)-(tsp-tst); Where f+(xi(t)) is the acceleration fitness value, t+(v0,xi(t),w0) is the acceleration time of the preset acceleration segment, tc(xi(t),w+) is the true speed cruise time of the preset acceleration segment, tsp is the on-time arrival time of the segment termination point, and tst is the on-time arrival time of the segment start point. Based on the aforementioned acceleration mathematical model, the following constraints are established for the acceleration mathematical model: st represents the constraint, f+(v0,vobj,w0) represents the acceleration fuel consumption, fc(vobj,dc,w+) represents the fuel consumption at the preset acceleration distance and true speed cruise, fmax represents the fuel carried by the flight platform at the starting point, d+(v0,vobj,w0) represents the acceleration distance, dc represents the true speed cruise distance, d represents the total distance of the flight segment, v0 represents the initial speed, v+boj represents the acceleration target speed, vmax represents the maximum speed of the flight platform, w0 represents the total weight of the flight platform when it starts cruise, and w+ represents the total weight of the flight platform when it starts acceleration. Based on the aforementioned deceleration mathematical model, the deceleration fitness value is set as f-(xi(t))=t-(v0,xi(t),w0)+tc(xi(t),w-)-(tsp-tst); where f-(xi(t)) is the deceleration fitness value, t-(v0,xi(t),w0) is the deceleration time of the preset deceleration segment, tc(xi(t),w-) is the true speed cruise time of the preset deceleration segment, tsp is the on-time arrival time of the segment termination point, and tst is the on-time arrival time of the segment start point; Based on the aforementioned deceleration mathematical model, the following constraints are established for the deceleration mathematical model: st represents the constraint, f-(v0,vobj,w0) represents the deceleration fuel consumption, fc(vobj,dc,w-) represents the fuel consumption at the preset deceleration distance during true-speed cruising, fmax represents the fuel carried by the flight platform at the starting point, d-(v0,vobj,w0) represents the deceleration distance, dc represents the true-speed cruising distance, d represents the total distance of the flight segment, v0 represents the initial speed, v-obj represents the deceleration target speed, vmin represents the minimum speed of the flight platform, w0 represents the total weight of the flight platform when it begins cruising, and w- represents the total weight of the flight platform when it begins deceleration.

4. The flight route on-time arrival planning method based on particle swarm optimization algorithm as described in claim 3, characterized in that, It also includes the following steps: Based on the planning particle swarm algorithm, the deceleration mathematical model is calculated. When the globally optimal deceleration particle and the current deceleration particle are on the same side of the preset ideal deceleration, the current deceleration particle is slowed down. Based on the planned particle swarm optimization algorithm, the deceleration mathematical model is calculated. When the globally optimal deceleration particle and the current deceleration particle are on opposite sides of the preset ideal deceleration, the current deceleration particle is accelerated by setting the deceleration particle speed. Wherein, vi- is the deceleration particle speed, gbest(t) is the globally preset optimal position, xi(t) is the preset position, f-(xi(t)) is the fitness of the preset position deceleration, and f(gbest(t)) is the fitness of the globally optimal solution. Based on the planning particle swarm algorithm, the acceleration mathematical model is calculated. When the globally optimal accelerating particle and the current accelerating particle are on the same side of the preset ideal acceleration, the current accelerating particle is slowed down. Based on the planned particle swarm algorithm, the acceleration mathematical model is calculated. When the globally optimal accelerating particle and the current accelerating particle are on opposite sides of the preset ideal acceleration, the current accelerating particle is accelerated by setting the acceleration particle speed, where vi+ is the acceleration particle speed, gbest(t) is the globally preset optimal position, xi(t) is the preset position, f+(xi(t)) is the preset position acceleration fitness, and f(gbest(t)) is the global optimal solution fitness.

5. The flight path on-time arrival planning method based on particle swarm optimization algorithm as described in claim 4, characterized in that, It also includes the following steps: Based on the aforementioned planning particle swarm algorithm, when the globally optimal particle converges to a preset ideal velocity and reaches a preset terminal position, the mathematical model is calculated to generate the terminal velocity; When the end velocity is greater than the ideal velocity, the preset end velocity value is decreased; when the end velocity is less than the ideal velocity, the preset end velocity value is increased. When the terminal velocity equals the ideal velocity, the planning particle swarm algorithm completes the calculation of the mathematical model, realizing the timely arrival of the flight route as planned.

6. The flight path on-time arrival planning method based on particle swarm optimization algorithm as described in claim 4, characterized in that, It also includes the following steps: When the current decelerating particle is less than the preset minimum speed of the flight platform, a signal is sent to the flight platform that the current route cannot be completed on time. When the current accelerating particle is greater than the preset maximum speed of the flight platform, a signal is sent to the flight platform that the current route cannot be completed on time.

7. A flight path on-time arrival planning device based on particle swarm optimization algorithm, characterized in that, The device includes: The mathematical model building module is used to plan preset flight segments based on flight platform performance. A particle swarm optimization (PSO) algorithm planning module is used to pre-set the PSO algorithm based on the mathematical model. The calculation module is used to perform calculations on the mathematical model based on the planning particle swarm algorithm to complete the planning of the flight route to arrive on time; it also includes: planning the route based on the performance of the flight platform to generate a first on-time arrival point and a second on-time arrival point; When the task time required to reach the second on-time arrival point from the first on-time arrival point is greater than or equal to the preset time, an acceleration point is inserted after the first on-time arrival point. Based on the acceleration point, an acceleration mathematical model is constructed: min|t+(v0,vobj,w0)+tc(vobj,w+)-(tsp-tst)|. Where t+(v0,vobj,w0) is the acceleration time, tc(vobj,w+) is the preset acceleration segment true speed cruise time, tsp is the time of arrival at the segment end point, and tst is the time of arrival at the segment start point. When the time required to travel from the first on-time arrival point to the second on-time arrival point is less than a preset time, a deceleration point is inserted after the first on-time arrival point. Based on the deceleration point, a deceleration mathematical model is constructed: min|t-(v0,vobj,w0)+tc(vobj,w-)-(tsp-tst)|; where t-(v0,vobj,w0) is the deceleration time, tc(vobj,w-) is the preset deceleration segment true speed cruise time, tsp is the on-time arrival time of the segment termination point, and tst is the on-time arrival time of the segment start point. It also includes: based on the mathematical model, presetting the particle velocity function of the particle swarm algorithm to generate a preset velocity function; Based on the mathematical model, the particle position function of the particle swarm algorithm is preset, and the preset position function is generated. Based on the preset velocity function and preset position function, a planning particle swarm algorithm is generated.

8. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 6.

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