CN117935625A - A smart air traffic drone route management system and method - Google Patents

Abstract

The present invention discloses a smart air traffic drone route management system and method, the system includes: a route structure division module, an intra-route operation management module and a route crossing operation management module, wherein: the route structure division module is used to demarcate a public pipeline space above the city, including an operation space, a protection space and an emergency disposal space; the intra-route operation management module is used to set the smart air traffic route interval, formulate emergency operation procedures and priority configuration; the route crossing operation management module is used to use a multi-machine artificial potential field + artificial bee colony optimization conflict resolution method to perform route crossing operation management when the flight sortie density is high and there is a route crossing. The present invention designs a route structure, and performs route crossing operation management through a multi-machine artificial potential field + artificial bee colony optimization conflict resolution method, which is convenient for automated conflict resolution, improves the automation level and safety level of smart air traffic, reduces the probability of collision, and improves the efficiency of route operation.

Description

A smart air traffic drone route management system and method

Technical Field

The present invention relates to the technical field of intelligent air traffic management, and more specifically to an intelligent air traffic unmanned aerial vehicle route management system and method.

Background technique

At present, unmanned aviation is developing rapidly and evolving iteratively. It has become a new mode of social life and economic production, representing the development trend of the global aviation industry.

Drones are highly digital, networked, and intelligent, and will continue to be integrated into the airspace system in the future. Many domestic drone logistics companies have already made great progress. However, the variety of drone types and complex scenarios have posed huge challenges to the traditional aviation regulatory system and technical means. How to ensure that multiple drones can fly without conflict in urban airspace and achieve safe and efficient urban air traffic management has become a common challenge faced by civil aviation management.

At present, urban air traffic supervision is mainly based on policies, lacking effective and complete service guarantees and technical means. Traditional air traffic management mainly adopts the method of providing control services based on sector controllers to achieve safe intervals and efficiency of high-density aircraft operations. General aviation mainly provides meteorological intelligence monitoring information through flight service agencies, and pilots fly visually to ensure safe intervals. However, compared with transport aviation and general aviation, smart city air traffic has strong digital and intelligent attributes in terms of operating environment, drones, operation management, service guarantee, etc. Therefore, the current air traffic management based on human decision-making and the infrastructure, information system, and service guarantee model of general aviation cannot meet the development requirements of smart city air traffic.

There is no mature and efficient drone air traffic management system for smart city air traffic operation scenarios such as drone logistics transportation and instant delivery in the existing technology, and there is no mature and efficient drone route management strategy. The existing management methods are very simple and lack automation settings. They require manual access, the technical conditions are weak, and the route operation efficiency needs to be improved. Therefore, new ideas and solutions need to be proposed in terms of large-scale, digital, and refined management of air traffic.

Summary of the invention

In view of this, the present invention provides an intelligent air traffic drone route management system and method that solves at least some of the above-mentioned technical problems, facilitates automated conflict resolution, improves automation and safety levels, reduces collision probability, and improves route operation efficiency.

To achieve the above object, the technical solution adopted by the present invention is:

In a first aspect, the present invention provides a smart air traffic drone route management system, the system comprising: a route structure division module, an intra-route operation management module and a route cross-operation management module, wherein:

The route structure division module is used to demarcate public pipeline space above the city, and the demarcated public pipeline space includes: operation space, protection space and emergency response space; the intra-route operation management module is used to set the smart air traffic route interval, formulate emergency operation procedures and priority configuration; the route crossing operation management module is used to use the multi-aircraft artificial potential field + artificial bee colony optimization conflict resolution method to perform route crossing operation management when the flight density is high and there is route crossing.

Furthermore, the operation space demarcated by the route structure division module adopts a nine-square grid operation mode.

Furthermore, the emergency operation procedure formulated by the route operation management module includes:

① When a special situation occurs on the route, the aircraft will immediately deviate from the route and enter the protection space. After the special situation is resolved, the aircraft will re-enter the operation space. If the operation cannot be resumed, the aircraft will make an emergency landing in the emergency handling space.

② The rule of one-side avoidance is adopted within the channel of the route; new drones entering the route must avoid other drones; drones changing their altitude within the route must avoid other drones; drones in the same direction behind the route must avoid the drones in front; overtaking in the same direction is allowed within the route, and overtaking is completed with the help of protection space.

Furthermore, the calculation method of the multi-aircraft artificial potential field adopted by the route crossing operation management module includes:

in, It represents the resultant force of the basic artificial repulsion on aircraft i; /> is the number of aircraft j that exerts artificial repulsion on aircraft i;/> Indicates the basic artificial repulsion; /> represents the base force acting on aircraft i; /> It indicates that aircraft i is subject to the force directed from the route entrance to the route exit; η indicates the repulsion gain; D indicates the distance between aircraft i and aircraft j; D_min indicates the minimum repulsion distance; D_max indicates the maximum repulsion distance; A_max indicates the maximum repulsion value.

Furthermore, the process of using artificial bee colony optimization in the route crossing operation management module includes:

1) Determine the optimization index

The optimization indicators include distance cost and time cost, where:

Distance cost:

in, It represents the total number of aircrafts that appear in the conflict resolution range within the time range T, i represents the corresponding aircraft; t represents the current time, /> Indicates the initial speed of aircraft i in the current integration segment, /> represents the mass of aircraft i, /> represents the superimposed artificial force of aircraft i,/> represents the base force acting on aircraft i; /> It means that before passing the intersection, the route number of aircraft i is p, and the channel number of the route is k./> It means that after passing the intersection, the route number of aircraft i is q, and the channel number of the route is m;/> is the expected flight distance of aircraft i; /> represents the weighting coefficient;

Time costs:

in, Indicates the actual flight length of aircraft i within the discrete calculation interval within the conflict resolution range,/> represents the actual flight length of aircraft i within the discrete calculation interval within the conflict resolution range,/> Indicates the maximum actual flight length of aircraft i within the conflict resolution range;/> Indicated in/> Actual flight time within the discrete calculation interval of the position; /> Indicates the end speed of the current discrete calculation interval; /> Indicates the initial speed of the current micro-region; /> represents the flight time of aircraft i on the expected route path within the conflict resolution range;

The final value function is:

Among them, W is a weighted function used to adjust the ratio between distance cost and time cost;

Assume superimposed artificial force , indicating the value that changes with the calculation interval; /> Respectively represent the forces in different coordinate directions; the optimization goal is to select the best sequence/> , the optimal solution Make/> Minimum;

2) Optimization of artificial bee colony method

① Initialize the population solution

Set the NS group population size and randomly generate the NS group initial population solution , each solution is a sequence of superimposed artificial forces/> , recorded as the initial nectar source of honey bees; calculate the value function J sequence of the initial honey bee population solution

Setting the fitness function , characterization/> The smaller, /> The larger the value

②Leading Bee Computing

Set up guide bees to search for new nectar sources

in, Represents a random number in [-1,1], /> ,/> , when the fitness function of the new nectar source is better than that of the old nectar source, the new nectar source replaces the old nectar source according to the greedy principle;

③Follow the bee calculation

Calculate follow probability ;

Set the follower bees to select the leader bee using a roulette wheel, that is, generate a uniformly distributed random number in [0,1]. If If it is greater than the random number, a new nectar source is generated, and the same calculation principle as the leading bee is used to determine whether to keep the nectar source;

④Scout bee calculation

Step ③ determines whether the nectar source meets the conditions for being abandoned; if so, the corresponding leading bee becomes a scout bee and searches for a new solution within the specified solution domain; otherwise, it directly goes to step ⑤;

⑤ Loop iteration

Determine whether the number of cycles is greater than the preset maximum value. If so, stop the population optimization iteration. If the change of is less than 1e-8, the population optimization iteration is stopped; if none of them are satisfied, the population optimization continues.

In a second aspect, the present invention further provides a smart air traffic drone route management method, which is applied to the above-mentioned smart air traffic drone route management system to achieve efficient management of smart air traffic drone routes, and the method includes:

Public pipeline space is demarcated above the city, and the public pipeline space demarcated includes: operation space, protection space and emergency disposal space;

When the UAV is operating in a route channel without crossing the route, the route operation management is carried out through the set route interval, emergency operation procedures and priority configuration;

When the flight density is high and there are crossing routes, the multi-aircraft artificial potential field + artificial bee swarm optimization conflict resolution method is used to manage route crossing operations.

Compared with the prior art, the present invention has at least the following beneficial technical effects:

The present invention provides an intelligent air traffic UAV route management system and method, the system comprising: a route structure division module, an intra-route operation management module and a route crossing operation management module; the present invention designs a route structure, defines the division of a safe area, and performs route crossing operation management through a multi-machine artificial potential field + artificial bee colony optimization conflict resolution method when there is a high flight density and route crossing, so as to facilitate automated conflict resolution, improve the automation level and safety level, reduce the collision probability, improve the route operation efficiency, and help to achieve safe and efficient route management in operational scenarios such as UAV logistics transportation and instant delivery.

Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

FIG1 is a schematic diagram of the structure of a smart air traffic drone route management system provided by an embodiment of the present invention.

FIG2 is a schematic diagram of a SAM route section model provided in an embodiment of the present invention.

FIG3 is a schematic diagram of a SAM route plane model provided by an embodiment of the present invention.

FIG. 4 is a schematic diagram of the operation mode of the nine-square grid provided by an embodiment of the present invention.

FIG5 is a schematic diagram of SAM route width/altitude provided in an embodiment of the present invention.

FIG6 is a schematic diagram of a first emergency operation procedure provided by an embodiment of the present invention.

FIG. 7 is a schematic diagram of a second emergency operation procedure provided by an embodiment of the present invention.

FIG8 is a schematic diagram of two intersecting routes provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

As shown in Figure 1, an embodiment of the present invention provides a smart air traffic UAV route management system, the system includes: a route structure division module, an intra-route operation management module and a route crossing operation management module, wherein: the route structure division module is used to demarcate a public pipeline space above the city, and the demarcated public pipeline space includes: operation space, protection space and emergency response space; the intra-route operation management module is used to set smart air traffic route intervals, formulate emergency operation procedures and priority configuration; the route crossing operation management module is used to use a multi-machine artificial potential field + artificial bee colony optimization conflict resolution method to perform route crossing operation management when the flight density is high and there is a route crossing.

The specific implementation of the present invention and the working principle of each functional module are described in detail below:

In the embodiment of the present invention, smart air traffic (SAM Smart air traffic) refers to a public pipeline space set aside over the city to meet the isolation operation of civil drones. SAM drones operate over the city and adopt SAM route operation rules to meet the beyond-visual-range operation requirements of SAM drones in the low-altitude airspace of the city.

1. Route structure division

In this embodiment, referring to Figures 2 and 3, the route structure division module demarcates a public pipeline space above the city, and the demarcated public pipeline space includes: operating space, protection space and emergency disposal space. Among them: the operating space is the space where the drone flies in the normal operating procedure. The protection space is the space that protects the abnormal operating procedure in the event of an accident, gust of wind, etc. The protection space can also be used as a temporary "borrowing passage" space for drones in the channel to overtake in the same direction. The emergency disposal space is the channel space that needs to fly out of the operating space when an emergency occurs to the drone, such as infrastructure failure, drone failure and forced landing. The intersecting routes in the plan view are cross routes. If there is a point of spatial intersection in the same altitude layer of the cross routes, the intersection point is recorded as the route intersection point.

In a specific embodiment, the operating space parameter setting of the SAM route is preferably determined according to the operating capability of the drone enterprise and the flight accuracy of the drone, and is repeatedly demonstrated through simulation and experimental testing. For example, for a multi-rotor drone with a maximum size of 2.3m, a cruising speed of 14m/s, a flight altitude of 200-300m, and a maximum take-off weight of 45kg, the normal operating space of the SAM route can be set as a "nine-square grid" with a cross-sectional area of 400 meters × 40 meters, as shown in Figure 4, and 9 "flight lanes" are drawn in the nine-square grid, numbered 1-9 respectively. The operating space adopts the operation mode of the nine-square grid, for example, using 1, 9 or 3, 7 bidirectional operation "flight lanes" to ensure that the bidirectional operation interval in the channel is the largest.

In the embodiment of the present invention, the size of the SAM route protection space mainly considers the risk buffer range of the UAV in an emergency situation, and the risk buffer includes air risk and ground risk. Still taking the above-mentioned operating parameters as an example, as shown in Figure 5, according to the standard of maximum flight altitude/protection zone width = 1, the SAM route protection zone width is preferably set to 300 meters. By setting the protection zone, the ground and air risks of the UAV after losing control can be greatly reduced. In summary, the SAM route protection zone is a normal operating space that extends 300 meters to the left and right and 10 meters up and down. Finally, the width of the SAM route = operating space width + protection zone width = 1000 meters, and the height of the SAM route = operating space height + protection zone height = 60 meters. The demarcation of the SAM route preferably comprehensively considers factors such as low-altitude airspace, ground risks, and support facilities. Considering that some airspaces are restricted by the geographical environment and airspace environment, the space size can be adjusted according to the operating airspace conditions, but the horizontal width is not less than 300 meters, and the operating procedures and access conditions are adjusted accordingly.

2. SAM route operation management

In the embodiment of the present invention, the interval between drones of the same operator within the SAM route may be the responsibility of the operator. The drones of different operators within the SAM route may preferably be arranged to maintain a horizontal interval of not less than 100 meters or a vertical interval of not less than 10 meters.

In an embodiment of the present invention, an emergency operation procedure is shown in FIG6 . When an emergency occurs on a SAM route, the operator needs to immediately deviate to the right from the route and enter the protection space, and then re-enter the operation space after the emergency situation is resolved. If the operation cannot be resumed, an emergency landing is required in the next emergency disposal space.

Another emergency operation procedure is shown in Figure 7. The principle of right-side avoidance is preferred within the SAM route. Newly entered drones within the route should avoid other drones. Drones changing altitude within the route should avoid other drones. Drones in the same direction behind should avoid drones in front. Overtaking in the same direction is allowed within the route, and overtaking can be completed with the help of protective space. The drone maintains vertical take-off and landing and enters or exits the channel after reaching the channel height, and pays attention to avoiding other flights inside and outside the channel.

3. SAM route crossing operation management

In the present invention, if a SAM route intersection occurs, it is divided into several situations:

1. The intersection points are layered in terms of height, that is, there is no intersection in space, so that the intersecting routes are distinguished in terms of height, and a 50m height interval margin is preferably reserved to avoid mid-air collisions of drones;

2. If the airspace is dense and it is impossible to operate in layers at altitude, that is, two or more channels intersect at the same altitude, there is a route intersection. If the flight density is very low, it can be preferred to use the "first come, first served" principle to determine whether there are drones in other channels operating 500m before the intersection. If not, pass through. If there are other drones passing, hover and wait until no one else passes before passing. If the flight density is high, use the multi-machine artificial potential field + artificial bee colony optimization conflict resolution method to pass through the intersection in a row.

The following focuses on the multi-machine artificial potential field + artificial bee colony optimization conflict resolution method used in the route crossing operation management module of the present invention:

As shown in Figure 8, take the intersection point as the center and take the The conflict resolution range is a circle. Consider a total of n intersecting routes (two intersecting routes in Figure 8 are common cases) intersecting at one point. The calculation starts from the moment any aircraft enters the conflict resolution range. Assume that the duration from the first aircraft entering to the last aircraft leaving is T (if the flight frequency is high, one day can be used as a calculation unit). At a certain moment, for a single aircraft i, due to the aircraft power and control system, aircraft i is subject to a force from the route entrance to the route exit/> In order to prevent collisions between multiple aircraft, the artificial potential field method is used in this embodiment to superimpose the basic artificial repulsion from other aircraft j on the control force of the aircraft/> The effect of (i.e. the resultant force of the basic artificial repulsion on aircraft i), /> is the number of aircraft within the range of D_max, that is, the number of aircraft j that may produce artificial repulsion on aircraft i. Then the basic resultant force on the aircraft is .

Basic artificial repulsion Calculation: First, define the repulsion distance threshold (D_min, D_max), repulsion gain η and peak value A_max between two aircraft (i and j). If the distance D between aircraft i and aircraft j is between D_min and D_max, let the repulsion/> If D is greater than D_max, the repulsive force is zero. If D is less than D_min, let /> =A_max, is the maximum repulsive force value. , the acceleration required for the aircraft to fly can be calculated to determine the value superimposed on the control force, which in turn affects the movement of the aircraft. When two aircraft approach, the force of the artificial potential field increases, causing the two aircraft to slow down. When the two objects maintain a safe distance, the artificial potential field force is eliminated. The control force needs to set an output limit to balance the relationship between power and repulsion, reduce excess, and adjust the distance of the artificial potential field force according to the conditions of aircraft of different weight levels to obtain the best control effect.

Superimposed artificial force Calculation of basic artificial repulsion: The calculation of basic artificial repulsion is equivalent to a fixed basic value, which is only related to the distance between aircraft and their own characteristics, but not to the traffic within the conflict resolution range. Therefore, it is necessary to introduce a superposition factor to comprehensively coordinate the overall traffic conditions within the conflict resolution range of the intersection. The optimization using the artificial bee colony method has the following steps:

1) Determine the optimization index

In this embodiment, the optimization index considers two factors: one is the distance factor. When an aircraft is at an intersection, there is a switching cost between the flight path of a certain route and the flight path of a certain route, and it is also necessary to avoid the flight flow of the intersecting heading. The longer the flight length to avoid or bypass, the greater the distance cost; the other is the time factor. When an aircraft passes through an intersection, in order to avoid conflicts with aircraft on other routes, it needs to slow down and wait or bypass, which will incur time costs. Among them:

Distance cost:

in, represents the total number of aircraft sorties that appear within the conflict resolution range within the time T, i represents

The calculation unit (or integration interval) corresponds to the aircraft. represents the sum of the distances traveled to avoid or circumvent the conflicting aircraft after taking into account the superposition factor, t represents the current moment, /> Represents the initial speed of the i-th aircraft in the current integration segment,/> represents the mass of the ith aircraft,/> represents the superimposed artificial force of the ith aircraft,/> Represents the basic force of the ith aircraft. /> represents the estimated lane change distance, where It means that before passing the intersection, the route number of aircraft i is p, and the channel number of this route is k (according to the above, there are only two options for k, either 1 or 7, or 3 or 9, which is related to the direction or reverse direction, and p is less than or equal to the total number of routes n),/> It means that after passing the intersection, the route number of aircraft i is q, the channel number of this route is m, and q is less than or equal to the total number of routes n./> Representative from/> Change lane to/> The shortest straight-line distance. /> Represents the sum of the distances that the aircraft is expected to travel within the conflict resolution range,/> is the expected flight distance of the ith aircraft, which can be determined based on the aircraft's route parameter settings before the flight. /> Represents the weighting coefficient, which is used to adjust the weights between avoiding, bypassing and changing lanes.

Time costs:

Among them, the formula The first part represents the actual flight time under the influence of superimposed artificial forces, and the second part represents the expected flight time, where /> is the actual flight length of the ith aircraft within the discrete calculation interval within the conflict resolution range,/> is the actual flight length of the ith aircraft within the discrete calculation interval within the conflict resolution range, is the maximum actual flight length of the ith aircraft within the conflict resolution range. /> Representatives in/> Actual flight time within the discrete calculation interval of the position, taking into account the superimposed artificial force in the non-uniform state/> It can be obtained by dividing the velocity deviation by the acceleration (estimated according to uniform acceleration), where/> is the end speed of the current discrete calculation interval, is the initial speed of the current micro-zone; in the uniform speed state, /> The actual flight length of the discrete calculation interval is divided by the current speed. /> is the actual flight time of the ith aircraft within the conflict resolution range, which can be solved using numerical calculation methods. /> Represents the sum of the expected flight time of aircraft within the conflict resolution range,/> is the flight time of the i-th aircraft on the expected route path within the conflict resolution range (without considering avoidance operations), which can be determined according to the route parameter setting value of the aircraft before the flight.

Final value function

W is a weighted function used to adjust the ratio between distance cost and time cost.

Assume superimposed artificial force , indicating the value that changes with the calculation interval; /> Represent the forces in different coordinate directions respectively;

The optimization goal is to select the appropriate Sequence /> , the optimal solution/> Make/> Minimum.

2) Optimize the steps using the artificial bee colony method

① Initialize the population solution

Set the NS group population size and randomly generate the NS group initial population solution (NS=50 can be set), each solution is the sequence value of superimposed artificial force/> , recorded as the initial nectar source of honey bees. Calculate the value function J sequence of the initial honey bee population solution

Setting the fitness function , characterization/> The smaller, /> The larger the value

②Leading Bee Computing

Set the leader bee to search for new nectar sources according to the following formula

is a random number in [-1,1], /> ,/> When the fitness function of the new nectar source is better than that of the old nectar source, the new nectar source replaces the old nectar source according to the greedy principle.

③Follow the bee calculation

Calculate follow probability .

Set the follower bees to select the leader bee using a roulette wheel, that is, generate a uniformly distributed random number in [0,1]. If If it is greater than the random number, then the following bee button/> The formula generates new nectar sources around nectar source i and uses the same calculation principle as the leading bee to determine whether to retain the nectar source.

④Scout bee calculation

From the previous step, determine whether nectar source i meets the conditions for being abandoned. If so, the corresponding leading bee role changes to a scout bee and searches for new solutions within the specified solution domain. Otherwise, go directly to step 5;

⑤ Loop iteration

Determine whether the number of cycles is greater than the maximum value (such as 500). If so, stop the population optimization iteration. The change of is less than 1e-8. If it is satisfied, stop the population optimization iteration. If none of them are satisfied, continue to optimize the population according to the above steps.

3) Obtain optimized superimposed artificial force results

According to the artificial bee colony algorithm, we get Relatively small solution/> , that is, to obtain the optimal value of the superimposed artificial force in each calculation interval/> .

Finally, the embodiment of the present invention carries out the simulation verification of the crossing route to verify whether the multiple drones can pass through the intersection smoothly without conflict and transfer to the new route. The optimization algorithm is verified to be effective, and the multiple drones can pass through the intersection smoothly without conflict and transfer to the new route.

From the description of the above embodiments, those skilled in the art can know that the present invention provides a smart air traffic drone route management system, which includes: a route structure division module, an intra-route operation management module and a route crossing operation management module, wherein: the route structure division module is used to demarcate a public pipeline space over the city, and the demarcated public pipeline space includes: an operation space, a protection space and an emergency disposal space; the intra-route operation management module is used to set the interval of smart air traffic routes, formulate emergency operation procedures and priority configuration; the route crossing operation management module is used to use a multi-machine artificial potential field + artificial bee colony optimization conflict resolution method to perform route crossing operation management when the flight sortie density is high and there is a route crossing. The present invention designs a route structure, defines the division of safe areas, and performs route crossing operation management through a multi-machine artificial potential field + artificial bee colony optimization conflict resolution method, which is convenient for automated conflict resolution, improves the automation level and safety level, reduces the probability of collision, and improves the route operation efficiency, which helps to achieve safe and efficient route management in operational scenarios such as drone logistics transportation and instant delivery.

Furthermore, an embodiment of the present invention also provides a smart air traffic drone route management method, which is applied to a smart air traffic drone route management system of the above embodiment to perform smart air traffic drone route management, and the method includes:

A public pipeline space is demarcated above the city, which includes: operation space, protection space and emergency response space; when the UAVs operate in the route channel without crossing the route, the route operation management is carried out through the set route intervals, emergency operation procedures and priority configuration; when the flight density is high and there is a route crossing, the multi-aircraft artificial potential field + artificial bee colony optimization conflict resolution method is adopted to manage the route crossing operation.

An intelligent air traffic drone route management method provided in an embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned system embodiment. For the sake of brief description, for parts not mentioned in this embodiment, please refer to the corresponding contents in the aforementioned system embodiment, and no further details will be given here.

In addition, an embodiment of the present invention also provides a storage medium on which one or more programs readable by a computing device are stored, and the one or more programs include instructions. When the instructions are executed by the computing device, the computing device executes the smart air traffic drone route management method in the above embodiment.

In the embodiment of the present invention, the storage medium may be, for example, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples of storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination thereof.

It should be understood by those skilled in the art that embodiments of the present invention may be provided as systems, methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several distinct components, and by means of a suitably programmed computer.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims (6)

1. An intelligent air traffic unmanned aerial vehicle route management system, which is characterized in that the system comprises: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module, wherein:

the route structure dividing module is used for dividing public pipeline space above a city, and the divided public pipeline space comprises: a run space, a protected space, and an emergency disposal space; the in-route operation management module is used for setting intelligent air traffic route intervals, making emergency operation programs and configuring priorities; the route cross operation management module is used for carrying out route cross operation management by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in the condition that the flight frame frequency density is higher.

2. The intelligent air traffic unmanned aerial vehicle route management system according to claim 1, wherein the operating space defined by the route structure dividing module adopts a nine-grid operating mode.

3. The intelligent air traffic unmanned aerial vehicle route management system of claim 1, wherein the emergency operation program formulated by the in-route operation management module comprises:

① When an emergency happens in the air route, the air route deviates from the air route immediately to enter a protection space, enters the operation space again after the special situation is relieved, and if the operation cannot be recovered, the emergency treatment space is reserved for descent;

② One side avoidance rule is adopted in the channel in the route; the unmanned aerial vehicle newly enters the air route to avoid other unmanned aerial vehicles; the unmanned aerial vehicle in the route changes the flying height and needs to avoid other unmanned aerial vehicles; the unmanned aerial vehicle is positioned at the rear in the same direction in the route to avoid the front unmanned aerial vehicle; the same direction overrun flight is allowed in the route, and the overrun is completed by means of the protection space.

4. The intelligent air traffic unmanned aerial vehicle route management system according to claim 1, wherein the calculation method of the multi-machine artificial potential field adopted by the route cross operation management module comprises the following steps:

；

；

；

wherein, Representing the resultant force of the basic artificial repulsive force applied by the aircraft i; /(I)The number of the aircraft j generating artificial repulsive force to the aircraft i; /(I)Representing a basic manual repulsive force; /(I)Representing the fundamental resultant force experienced by aircraft i; /(I)Indicating that aircraft i is subject to power directed from the airline entrance to the airline exit; η represents the rejection gain; d represents the distance between aircraft i and aircraft j; d_min represents the minimum value of the rejection distance; d_max represents the maximum value of the repulsive distance; a_max represents the maximum repulsive force value.

5. The intelligent air traffic unmanned aerial vehicle route management system of claim 4, wherein the route cross-over operation management module employs artificial bee colony optimization comprising:

1) Determining an optimization index

The optimization metrics include distance cost and time cost, wherein:

Distance cost:

；

wherein, Representing the total number of aircraft frames occurring in a conflict resolution range within a time T, and i represents a corresponding aircraft; t represents the current time,/>Representing the initial velocity of the current product segment of aircraft i,/>Representing the mass of aircraft i,/>Representing superimposed artificial forces of aircraft i,/>Representing the fundamental resultant force experienced by aircraft i; /(I)Indicating that the aircraft i is located on the route p before passing through the intersection, and the route on the route is located on the route k,/>After the aircraft passes through the intersection, the route number of the aircraft i is q, and the route number of the aircraft i is m; /(I)Is the expected flight distance of aircraft i; /(I)Representing the weighting coefficients;

Time cost:

；

；

wherein, Representing the length of aircraft i that has actually flown within the discrete calculation interval within the conflict resolution range,Representing the actual flight length of aircraft i within the discrete computation interval within the conflict resolution range,/>Representing the maximum value of the actual flight length of aircraft i within the conflict resolution range; /(I)Expressed at/>Actual flight time in a discrete calculation interval of the position; /(I)Representing the ending speed of the current discrete computation interval; /(I)Representing an initial velocity of the current differential interval; /(I)Representing a time of flight of aircraft i on a desired course path within the conflict resolution range;

The final cost function is:

；

Wherein W is a weighting function for allocating the ratio between the distance cost and the time cost;

Superimposed artificial force A value representing a change with the calculation section; /(I)Respectively representing forces in different coordinate directions; the optimization target is to select the optimal sequence/>Solution is optimalMake/>Minimum;

2) Artificial bee colony method optimization

① Initializing population solutions

Setting the population scale of the NS group, and randomly generating initial population solutions of the NS groupEach solution is a sequence value of superimposed artificial force/>The honey is marked as an initial honey source of the bees; calculating the cost function J sequence of initial bee population solution

；

Setting fitness functionsCharacterization/>Smaller,/>The greater the value

；

② Leading bee calculation

Setting leading bees to search new honey sources；

；

Wherein,Representing random numbers within [ -1,1 ]/>，/>When the fitness function of the new honey source is superior to that of the old honey source, enabling the new honey source to replace the old honey source according to the greedy principle;

③ Following bee calculation

Calculating the following probability；

The following bees are arranged to select the leading bees by roulette, i.e. to generate a random number of uniform distribution in [0,1], ifIf the number is larger than the random number, generating a new honey source, and judging whether to reserve the honey source by using the same calculation principle as that of the leading bees;

④ Calculation of scout bees

Judging whether the honey source meets the abandoned condition or not by the step ③; if yes, changing the corresponding leading bee role into a reconnaissance bee, searching a new solution in a specified solution domain, otherwise, directly transferring to a step ⑤;

⑤ Iteration of the loop

Judging whether the cycle times are greater than a preset maximum value, if so, stopping population optimization iteration, or if soIf the variation of (2) is less than 1e-8, stopping population optimization iteration; if none of them is satisfied, the population continues to be optimized.

6. A method for managing intelligent air traffic unmanned aerial vehicle airlines, which is applied to the intelligent air traffic unmanned aerial vehicle airlines management system as defined in any one of claims 1 to 5, and comprises the following steps:

a public pipeline space is marked up in the urban overhead space, and the marked public pipeline space comprises: a run space, a protected space, and an emergency disposal space;

when the unmanned aerial vehicle runs in the route channel without crossing, the route running management is carried out through the set route interval, the emergency running program and the priority configuration;

When the flight frame has higher secondary density and has the route crossing, the route crossing operation management is carried out by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method.