CN110958574B - Unmanned aerial vehicle-based real-time repair method for road connectivity of vehicle self-organizing network - Google Patents

Unmanned aerial vehicle-based real-time repair method for road connectivity of vehicle self-organizing network Download PDF

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CN110958574B
CN110958574B CN201911162112.6A CN201911162112A CN110958574B CN 110958574 B CN110958574 B CN 110958574B CN 201911162112 A CN201911162112 A CN 201911162112A CN 110958574 B CN110958574 B CN 110958574B
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蔡震
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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    • H—ELECTRICITY
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    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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Abstract

The invention discloses a real-time repairing method for road connectivity of a vehicle self-organizing network based on an unmanned aerial vehicle. The method mainly comprises the steps that data such as the real-time position, the free flow speed and the like of a current road vehicle are obtained based on an unmanned aerial vehicle; calculating the position of each moment in the next period of the vehicle according to the acquired information, and predicting the road multi-hop network communication condition between two intersections according to the position; and if the regional network is disconnected on the road in the future period, the unmanned aerial vehicle carries out bridging repair by using a decision-making calculation optimal period movement strategy according to the current condition. According to the mechanism method, only a single unmanned aerial vehicle is required to be configured on a single road to collect vehicle information by means of a vehicle self-organizing network, the future motion track of the vehicle is calculated to predict the position of a network disconnection area, the current unmanned aerial vehicle is combined to calculate the optimal real-time movement strategy, the disconnection area which is possibly generated in the future period is repaired to the maximum extent, and when the traffic flow is small, namely when the number of vehicle nodes is small, stable and effective network connectivity support is provided for road vehicle communication.

Description

Unmanned aerial vehicle-based real-time repair method for road connectivity of vehicle self-organizing network
Technical Field
The invention relates to the technical field of vehicle networking, in particular to a real-time repairing method for road connectivity of a vehicle self-organizing network based on an unmanned aerial vehicle.
Background
The goal of the internet of vehicles (IoV) is to realize the connection and communication among multiple vehicles, multiple persons, multiple objects and various networks, and to provide a controllable, operable and safe wide area network covering the whole city, and its specific applications include safety-related (automatic driving, accident avoidance), information broadcasting (real-time traffic information), social entertainment and so on. Each vehicle node in the internet of vehicles is equipped with a transceiver that supports the relevant network standards (such as DSRC in the united states, LTE-V in china, etc.). Vehicle ad hoc networks (VANETs) are the names of car networking in the category of networking forms, and as a special form of mobile ad hoc networks (MANETs), have the following three characteristics: 1) the node moving speed is high, and the network topology changes frequently; 2) because the vehicle can only run on the existing road, the transmission direction of the data packet between the vehicles is consistent with the direction of the urban road; 3) data packet communication of two vehicle nodes is often finished through multi-hop sequential transmission of a plurality of relay vehicle nodes, and the routing quality of the data packet communication is easily influenced by multiple factors such as road vehicle density, vehicle speed, intersection signal lamp states and the like.
Compared with a cellular network and a wired network, the vehicle self-organizing network has the advantages of no transmission cost, low deployment cost and the like, meanwhile, the point-to-point networking form can effectively reduce the network bottleneck and increase the network throughput rate, however, because the network topology changes frequently, an effective and stable multi-hop network path cannot be kept among each group of vehicle nodes, and particularly when the distance between any group of continuous vehicles on a road exceeds a single-hop transmission range, the road network (between two end intersections) cannot be communicated.
To fully utilize free ad hoc networks, it is necessary to
To enhance or repair road connectivity, a common solution is to install a fixed device, i.e. a Road Side Unit (RSU), with the same network communication standard at a key location on the road side, and the RSU serves as a relay node to transmit data packets. However, the method not only occupies ground space and has high deployment cost, but also has frequent change of dynamic traffic flow on the road along with time, changes of network disconnection positions all the time, and cannot be effectively adapted by deploying fixed nodes. In order to improve the dynamic effectiveness of road connectivity repair, however, limited to conditions such as road traffic regulations and surrounding vehicle density, it is always difficult for a ground vehicle to reach a disconnected position in time to repair connectivity according to the change of the current road network.
In recent years, Unmanned Aerial Vehicle (UAV) technology has received increasing attention, and its free space deployment capability and flexible space movement capability have enabled it to be widely used in a large number of fields. In a vehicle self-organizing network environment, Oubbati et al propose respective architectures and schemes for assisting vehicle nodes to improve network performance and increase network throughput rate by using unmanned aerial vehicle nodes in papers. However, the unmanned aerial vehicle only utilizes the space deployment capability of the unmanned aerial vehicle, but does not fully utilize the flexible movement capability of the unmanned aerial vehicle to assist in enhancing the road connectivity, and does not relate to a scheme for making future trajectory decisions aiming at road traffic flow conditions changing at any moment, so that the requirements on dynamic property and real-time property cannot be met.
Disclosure of Invention
The invention aims to solve the technical problem of providing a real-time repairing method for the road connectivity of a vehicle self-organizing network based on an unmanned aerial vehicle.
In order to solve the technical problems, the technical scheme adopted by the invention is a real-time repairing method for the road connectivity of a vehicle self-organizing network based on an unmanned aerial vehicle, which comprises the following steps:
1) the unmanned aerial vehicle selects one of the intersections at the two ends of the road as a dependent intersection, and takes the guarantee of multi-hop network communication with a service node of the dependent intersection as a primary task of a mobile strategy;
2) the unmanned aerial vehicle acquires the real-time position of the current road vehicle and the free flow speed data information thereof to the maximum extent through the vehicle self-organizing network;
mean value mu from the vehicle free flow speedfvSum variance
Figure GDA0002798026100000033
Future time position pos of vehicle in free flow state with small traffic flowestMean value of (a)posSum variance
Figure GDA0002798026100000034
The calculation formula of (2) is as follows:
μpos＝E(posest)＝μfv·(tfut-trec) (1)
Figure GDA0002798026100000031
wherein, tfutFor a future time, trecObtaining the time for the vehicle information; in general, the speed of a vehicle in a free-stream state follows a normal distribution, so posestAlso subject to a normal distribution, the corresponding distances dis between two consecutive vehicles A, B, independent of each otherABAlso obey a normal distribution:
Figure GDA0002798026100000032
3) the unmanned aerial vehicle calculates the position of each unit time in the next period of all vehicles on the road according to the acquired information, calculates the probability of the occurrence of a network disconnection area and the distance between the area and a supported intersection, then obtains the repair priority of the area according to the two points, and selects the area with the largest priority value as the first-choice repair area at the unit moment;
the calculation formula of the repair priority pri of the network disconnection area at unit time is as follows:
Figure GDA0002798026100000041
Figure GDA0002798026100000042
Figure GDA0002798026100000043
wherein, PABThe probability of occurrence of a disconnection region, h, that a distance between consecutive vehicles A, B exceeds the signal communication distance RUAVFor unmanned flight altitude, l is road length (μ)posA+μposB) 2, the mean value of the central position of the network disconnection area; alpha and beta are weight factors, and alpha + beta is 1, the larger the alpha is set, the more the strategy is biased to the connectivity of the unmanned aerial vehicle and the supported intersection, and the larger the beta is set, the more the strategy is focused to repair the area with high disconnection probability, although the distance from the supported intersection is possibly far;
4) if the regional network is disconnected on the road in the future period, the unmanned aerial vehicle starts to make a decision to calculate the optimal periodic movement strategy according to the current condition so as to achieve the maximum road network connectivity restoration effect; the unmanned aerial vehicle sequentially calculates reachable state information of each unit moment of the next period according to the current position, the speed and the moving direction of the unmanned aerial vehicle;
if the reachable area of the unmanned aerial vehicle at a certain unit moment is overlapped with the disconnected area of the vehicle network at the moment, the unmanned aerial vehicle can move to the area and repair the bridge connection of the front node and the rear node, and the reachable state information of the unit moment is modified by the unmanned aerial vehicle according to the overlapped part;
if the accessible area of the unmanned aerial vehicle is not overlapped with the disconnection area of the vehicle network, the priority scores of the disconnection area of the network and the previous overlapped repairable disconnection area are required to be compared, if the priority of the disconnection area of the previous moment is high, the disconnection area repair of the current moment is abandoned, the backward calculation is continued, if the priority of the disconnection area of the network of the current moment is high, the disconnection area repair of the previous moment is abandoned, the accessible state of the current moment is recalculated according to the state of the unmanned aerial vehicle before the previous moment, and whether the disconnection area can be repaired is determined again;
representation method of reachable state of unmanned aerial vehicle at certain moment and based on t0The state of the time is calculated to the next time t1A calculation formula of the reachable state;
reachable status information includes
Figure GDA0002798026100000051
Wherein
Figure GDA0002798026100000052
And
Figure GDA0002798026100000053
is t0The left and right edge positions that can be reached at the moment,
Figure GDA0002798026100000054
the minimum speed achievable, i.e. the maximum speed in the left direction, is here uniformly expressed as a vector,
Figure GDA0002798026100000055
in order to be able to achieve the maximum speed,
Figure GDA0002798026100000056
for the maximum position where the unmanned aerial vehicle can reach the minimum speed, the same principle is adopted
Figure GDA0002798026100000057
The minimum position where the unmanned aerial vehicle can reach the maximum speed; the calculation formula of each state information is as follows:
Figure GDA0002798026100000058
Figure GDA0002798026100000059
Figure GDA00027980261000000510
Figure GDA00027980261000000511
Figure GDA00027980261000000512
Figure GDA00027980261000000513
wherein, tarFor unmanned aerial vehicle from t0Velocity of time of day
Figure GDA00027980261000000514
Accelerating to its maximum speed UvmaxRequired time, talFor unmanned aerial vehicle from
Figure GDA00027980261000000515
Accelerate to its minimum speed Uvmin(Uvmax＝-Uvmin) The required time, a, is scalar acceleration;
Figure GDA0002798026100000061
Figure GDA0002798026100000062
Figure GDA0002798026100000063
Figure GDA0002798026100000064
wherein, tdlAnd tdrThe time required for subtracting the acceleration of the unmanned aerial vehicle to the minimum speed and the maximum speed respectively, which is less than 0 means that the unmanned aerial vehicle cannot accelerate to the physical maximum speed thereof in unit time;
when the reachable area of the unmanned aerial vehicle overlaps with the disconnected area of the vehicle network, the reachable state information of the unmanned aerial vehicle is corrected
Figure GDA0002798026100000065
The calculation formula of (2):
Figure GDA0002798026100000071
Figure GDA0002798026100000072
Figure GDA0002798026100000073
Figure GDA0002798026100000074
Figure GDA0002798026100000075
Figure GDA0002798026100000076
wherein, tl1And tl2Two positive numerical solutions for t, for equation (18)r1And tr2Two positive numerical solutions for t for equation (20);
5) and finally, after all unit time in the next period is calculated, the unmanned aerial vehicle movement strategy in the period can be sequentially generated according to the time reverse order according to the reachable state updated in each unit time.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the invention designs an unmanned aerial vehicle node movement decision mechanism which is suitable for repairing the road connectivity of the vehicle self-organizing network in real time in the road vehicle density sparse environment by utilizing the characteristics of convenient deployment, strong space capability, flexible and free movement and the like of the unmanned aerial vehicle. The method is characterized in that only a single unmanned aerial vehicle is configured on a single road, vehicle information is collected by means of a vehicle self-organizing network, the future motion trail of the vehicle is calculated to predict the position of a network disconnection area, and an optimal real-time movement strategy is calculated by combining the current position, direction and speed of the unmanned aerial vehicle, so that the disconnection area which possibly appears in the future period is repaired to the maximum extent.
Drawings
FIG. 1 is a schematic diagram of the invention, namely an Unmanned Aerial Vehicle (UAV) real-time patching road network connectivity;
FIG. 2 is a schematic diagram of an unmanned aerial vehicle of the present invention collecting real-time vehicle information from a road;
FIG. 3 is a schematic illustration of a vehicle repairable distance and an unrepairable distance before and after the present invention;
FIG. 4 is a schematic view of the flight trajectory of the unmanned aerial vehicle of the present invention;
fig. 5 is a schematic view of the reachable position of the drone of the present invention;
fig. 6 is a logic diagram of the unmanned aerial vehicle movement decision process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the invention discloses a real-time repairing method for road connectivity of a vehicle ad hoc network based on an unmanned aerial vehicle, which comprises the following steps:
1) the unmanned aerial vehicle selects one of the intersections at the two ends of the road as a dependent intersection, and takes the guarantee of multi-hop network communication with a service node of the dependent intersection as a primary task of a mobile strategy;
2) the unmanned aerial vehicle acquires data information such as the real-time position of the current road vehicle and the free flow speed of the current road vehicle to the maximum extent through a vehicle self-organizing network;
3) the unmanned aerial vehicle calculates the position of each unit time in the next period of all vehicles on the road according to the acquired information, calculates the probability of the occurrence of a network disconnection area and the distance between the area and a supported intersection, then obtains the repair priority of the area according to the two points, and selects the area with the largest priority value as the first-choice repair area at the unit moment;
4) if the regional network is disconnected on the road in the future period, the unmanned aerial vehicle starts to make a decision to calculate the optimal periodic movement strategy according to the current condition so as to achieve the maximum road network connectivity restoration effect; the unmanned aerial vehicle sequentially calculates reachable state information of each unit moment of the next period according to the current position, the speed and the moving direction of the unmanned aerial vehicle; if the reachable area of the unmanned aerial vehicle at a certain unit moment is overlapped with the disconnected area of the vehicle network at the moment, the unmanned aerial vehicle can move to the area and repair the bridge connection of the front node and the rear node, and the reachable state information of the unit moment is modified by the unmanned aerial vehicle according to the overlapped part; if the accessible area of the unmanned aerial vehicle is not overlapped with the disconnection area of the vehicle network, the priority scores of the disconnection area of the network and the previous overlapped repairable disconnection area are required to be compared, if the priority of the area of the previous moment is high, the disconnection area repair of the current moment is abandoned, the backward calculation is continued, if the priority of the disconnection area of the current moment is high, the area repair of the previous moment is abandoned, the accessible state of the current moment is recalculated according to the state of the unmanned aerial vehicle before the previous moment, and whether the disconnection area can be repaired is determined again.
5) And finally, after all unit time in the next period is calculated, the unmanned aerial vehicle movement strategy in the period can be sequentially generated according to the time reverse order according to the reachable state updated in each unit time.
Example 1
It is assumed that each running vehicle is equipped with a communication transceiver supporting the relevant vehicle network standard, and is equipped with a Global Positioning System (GPS) and an electronic map of urban roads. In the aspect of unmanned aerial vehicles, standby unmanned aerial vehicles can be arranged on each road for rotation, so that the problem of power consumption of the unmanned aerial vehicles is not considered for the moment; the flight speed of the unmanned aerial vehicle is different according to different manufacturers and models, the unmanned aerial vehicle can not catch up with vehicles, but can reach a target position by maximally utilizing the performance of the unmanned aerial vehicle to repair a road network, and although the speed has an objective influence on the repair effect, the speed is not the core problem of the unmanned aerial vehicle. In addition, in order to research the road network in a targeted manner, it is assumed that intersection service nodes exist at intersection positions at both ends of the road network (the main routing mechanism of the vehicle ad hoc network is that intersection service nodes responsible for routing data packets are arranged at the intersection positions, and the main routing mechanism can be generally served by fixed roadside nodes or dynamic vehicle nodes).
When the road traffic flow is in a free flow state, namely the density of surrounding vehicles is low, the current vehicle is not influenced by other vehicles and can run at the expected speed per se, and the speed is called as the free flow speed of the vehicle. However, the speed value is often not constant throughout the driving process and is influenced by various factors including the current time, the current road location and even the drivingThe current mood of the person (the speed value of the autonomous vehicle is relatively stable) is generally subject to a normal distribution. Under the mechanism of the invention, each vehicle automatically collects the self free flow velocity value at ordinary times and calculates the mean value mu of the self free flow velocity valuefvSum variance
Figure GDA0002798026100000101
Through the interaction of periodic information (namely hello information, belonging to the requirements of the mobile ad hoc network standard), each vehicle node can obtain the relevant information of all the neighbor vehicles in the hop transmission range (R) of the vehicle node, including vehicle ID, position, speed, direction and the like, and the free flow speed (mean value and variance) of each vehicle is also packaged in the information and is exchanged with each other. In the mechanism, in order to collect real-time information of vehicles on the whole road, an unmanned aerial vehicle needs to periodically send data packets (ICP) for information collection to intersection service nodes at two ends of the road; and the vehicle node at the farthest end in each direction is selected as a receiving node, and after receiving the ICP, the receiving node writes the information of the receiving node and the neighbor nodes into a data packet and then continues to send the information to the directions of the two ends. And after the ICP reaches the intersection range, the intersection service node is preferred as a receiving node, and after the information is written in, the ICP is returned to the unmanned aerial vehicle node in the opposite direction. If no relay vehicle node is available in front in the ICP transmission process, the vehicle node returns to the unmanned aerial vehicle node immediately. So far, as shown in fig. 2, through ICP multi-hop transmission, the unmanned aerial vehicle can obtain real-time information of vehicles on the current road to the maximum extent.
According to the collected information, the future time position pos of the vehicle in the free flow stateestThe calculation formula is as follows:
μpos＝E(posest)＝μfv·(tfut-trec) (1)
Figure GDA0002798026100000111
wherein, tfutFor a future time, trecFor new acquisition of time of day, posestAlso obey normalDistribution, the distances between the corresponding front and rear vehicle nodes which are independent of each other also follow normal distribution:
Figure GDA0002798026100000112
as shown in fig. 3, the network connection relationship of the front and rear vehicles at the future time can be divided into 3 cases according to the distance:
1)disABr is less than or equal to R, and the front vehicle and the rear vehicle are in respective single-hop transmission ranges, namely the connection is available;
2)R<disABthe transmission rate is less than or equal to 2R, the front vehicle and the rear vehicle are not in the respective single-hop transmission range, and the connection can be repaired by arranging a relay node in the middle;
3)disAB>2R, front and rear vehicles are not in the respective single-hop transmission range and cannot be repaired.
Considering the flying height h of the unmanned planeUAVAnd after calculating the horizontal direction distance R 'of the transmission distance, the applicable environment is repaired by the unmanned aerial vehicle, namely the probability that the distance between the front and rear vehicles A, B is greater than the single-hop transmission distance R and less than 2R' is as follows:
Figure GDA0002798026100000113
Figure GDA0002798026100000121
Figure GDA0002798026100000122
can derive PABLarger means that the front and rear vehicles lose connection, but the larger the possibility that a single relay node is intermediately provided, i.e., repairable.
Before a mobile strategy is calculated, an unmanned aerial vehicle needs to select one of intersections at two ends of a road as a supported intersection, and the service node of the supported intersection is kept in multi-hop network communication to serve as a primary task, so that an area which is close to the supported intersection and has a higher disconnection probability is repaired by priority, and a disconnection area repair priority (pri) formula is given:
Figure GDA0002798026100000123
wherein l is the road length (μ)posA+μposB) And 2 is the mean value of the central positions of the network disconnection areas, and alpha and beta are weight factors.
The single cycle is divided into n equal unit times: t is ti,(i∈[1,n]) After the periodic information collection data packet is returned, the unmanned aerial vehicle sequentially calculates the disconnection area pri with the highest priority in each unit time of the next periodiAnd then starts to calculate the motion strategy within the cycle. Assuming that the road is in the horizontal direction, the left side intersection is the unmanned aerial vehicle supported intersection and the position of the left side intersection is 0, and the road positions are sequentially increased to the right.
From the current time t0Initially, according to the current speed of the drone
Figure GDA0002798026100000124
And position
Figure GDA0002798026100000125
The decision-making process calculates the reachable state of the unmanned aerial vehicle at each moment in turn according to unit time recursion
Figure GDA0002798026100000126
Figure GDA0002798026100000127
Wherein
Figure GDA0002798026100000128
And
Figure GDA0002798026100000129
is t0The left and right edge positions that can be reached at the moment,
Figure GDA00027980261000001210
the minimum speed achievable, i.e., the maximum speed in the left direction, where speeds are uniformly expressed as vectors,
Figure GDA00027980261000001211
in order to be able to achieve the maximum speed,
Figure GDA00027980261000001212
for the maximum position where the unmanned aerial vehicle can reach the minimum speed, the same principle is adopted
Figure GDA0002798026100000131
The minimum position at which the drone can reach maximum speed. Unmanned aerial vehicle slave t1The time reachable state calculation formula is as follows:
Figure GDA0002798026100000132
Figure GDA0002798026100000133
Figure GDA0002798026100000134
Figure GDA0002798026100000135
Figure GDA0002798026100000136
Figure GDA0002798026100000137
wherein, tarFor unmanned aerial vehicle from t0Velocity of time of day
Figure GDA0002798026100000138
Accelerating to its maximum speed UvmaxRequired time, talFor unmanned aerial vehicle from
Figure GDA0002798026100000139
Accelerate to its minimum speed Uvmin(Uvmax＝-Uvmin) The required time, a, is the scalar acceleration.
Figure GDA0002798026100000141
Figure GDA0002798026100000142
Figure GDA0002798026100000143
Figure GDA0002798026100000144
Wherein, tdlAnd tdrThe time required to subtract the acceleration of the drone to the minimum and maximum speeds, respectively, which is less than 0 means that the drone cannot accelerate to its physical maximum speed per unit of time. Below with tdrAnd
Figure GDA0002798026100000145
for example, the formula is explained (t)dlAnd
Figure GDA0002798026100000146
similarly, the description is not repeated).
From the above formula, when
Figure GDA0002798026100000147
When is, i.e. t0The flying direction of the unmanned aerial vehicle is left at the moment, tdr<0 represents (t)0，t1) Inner unmanned plane can not accelerate to rightPhysical maximum velocity, so that it can reach the maximum velocity to the right, i.e. the maximum vector velocity, of
Figure GDA0002798026100000148
And is
Figure GDA0002798026100000149
Is equal to
Figure GDA00027980261000001410
tdr>0 is (t)1-t0)>tarIs shown at (t)0，t1) The inner unmanned aerial vehicle always accelerates rightwards, and can reach the physical maximum speed, and the position of the unmanned aerial vehicle when the unmanned aerial vehicle finishes using the rightwards maximum acceleration is the position
Figure GDA00027980261000001411
When in use
Figure GDA00027980261000001412
And t isdr>0 (which can accelerate to physical maximum speed),
Figure GDA00027980261000001413
the solving process is complex, the unmanned aerial vehicle needs to complete 6 steps as shown in fig. 4, firstly, the unmanned aerial vehicle needs to accelerate to the left, and the current speed in the right direction is reduced to 0 (step 1); then continuously accelerating leftwards to increase the speed to
Figure GDA0002798026100000151
(step 2); step 3, the unmanned aerial vehicle continuously accelerates to the maximum left-direction speed allowed by time and then descends to
Figure GDA0002798026100000152
The process of (2); the maximum left speed in this process is based on the duration tdrDepending on the limit of (t)drWhen the temperature is less than or equal to 0, the step is not needed; in the steps 4 and 5, the unmanned aerial vehicle accelerates to the right, reduces the speed to 0 and then promotes to the speed
Figure GDA0002798026100000153
Finally accelerating to Uv to the rightmaxThe required time is tarFinally tdrI.e. the unit time minus talAnd
Figure GDA0002798026100000154
computation completion t1After the state can be reached all the time, if a repairable disconnection area exists at the moment, the repairable area and the reachable area of the unmanned aerial vehicle are repaired
Figure GDA0002798026100000155
There is overlap, that is, it means that the drone can move to the area and repair the front and back node connection (as shown in fig. 5), and then according to the overlap area
Figure GDA0002798026100000156
Updating t1Reachable state of unmanned aerial vehicle at any moment
Figure GDA0002798026100000157
Figure GDA0002798026100000158
Figure GDA0002798026100000159
Figure GDA00027980261000001510
Figure GDA00027980261000001511
Figure GDA00027980261000001512
Figure GDA00027980261000001513
Figure GDA00027980261000001514
Wherein, tl1And tl2Two positive numerical solutions for t, for equation (18)r1And tr2Are two positive numerical solutions of equation (20) with respect to t.
Based on t1The reachable state after the update is carried out at all times, the unmanned aerial vehicle calculates and updates the reachable state of each unit according to the process in sequence, and if t is carried outiAt the moment, the reachable area of the unmanned aerial vehicle is not overlapped with the disconnected area, namely, the unmanned aerial vehicle cannot be repaired, the priority scores of the disconnected area and the previous overlapped repairable disconnected area need to be compared, and if the priority of the previous area is high, t is abandonediContinuously calculating backward at the moment; if t isiIf the time break area has high priority, abandoning the repair area at the previous time, and calculating and judging t according to the reachable state at the previous timeiWhether or not the time of day zone is repairable (refer to fig. 5). After all unit time in the final period is calculated, the unmanned aerial vehicle movement strategy in the period can be sequentially generated according to the time reverse order according to the reachable state after each unit time is updated.

Claims (1)

1. A real-time repairing method for road connectivity of a vehicle self-organizing network based on an unmanned aerial vehicle is characterized in that: the method comprises the following steps:
1) the unmanned aerial vehicle selects one of the intersections at the two ends of the road as a dependent intersection, and takes the guarantee of multi-hop network communication with a service node of the dependent intersection as a primary task of a mobile strategy;
2) the unmanned aerial vehicle acquires the real-time position of the current road vehicle and the free flow speed data information thereof to the maximum extent through the vehicle self-organizing network;
mean value mu from the vehicle free flow speedfvSum variance
Figure FDA0002798026090000011
Free flow of vehicle when vehicle flow is smallFuture time position pos in stateestMean value of (a)posSum variance
Figure FDA0002798026090000012
The calculation formula of (2) is as follows:
μpos＝E(posest)＝μfv·(tfut-trec) (1)
Figure FDA0002798026090000013
wherein, tfutFor a future time, trecObtaining the time for the vehicle information; in general, the speed of a vehicle in a free-stream state follows a normal distribution, so posestAlso subject to a normal distribution, the corresponding distances dis between two consecutive vehicles A, B, independent of each otherABAlso obey a normal distribution:
Figure FDA0002798026090000014
3) the unmanned aerial vehicle calculates the position of each unit time in the next period of all vehicles on the road according to the acquired information, calculates the probability of the occurrence of a network disconnection area and the distance between the area and a supported intersection, then obtains the repair priority of the area according to the two points, and selects the area with the largest priority value as the first-choice repair area at the unit moment;
the calculation formula of the repair priority pri of the network disconnection area at unit time is as follows:
Figure FDA0002798026090000021
Figure FDA0002798026090000022
Figure FDA0002798026090000023
wherein, PABThe probability of occurrence of a disconnection region, h, that a distance between consecutive vehicles A, B exceeds the signal communication distance RUAVFor unmanned flight altitude, l is road length (μ)posA+μposB) 2, the mean value of the central position of the network disconnection area; alpha and beta are weight factors, and alpha + beta is 1, the larger the alpha is set, the more the strategy is biased to the connectivity of the unmanned aerial vehicle and the supported intersection, and the larger the beta is set, the more the strategy is focused to repair the area with high disconnection probability, although the distance from the supported intersection is possibly far;
4) if the regional network is disconnected on the road in the future period, the unmanned aerial vehicle starts to make a decision to calculate the optimal periodic movement strategy according to the current condition so as to achieve the maximum road network connectivity restoration effect; the unmanned aerial vehicle sequentially calculates reachable state information of each unit moment of the next period according to the current position, the speed and the moving direction of the unmanned aerial vehicle;
if the reachable area of the unmanned aerial vehicle at a certain unit moment is overlapped with the disconnected area of the vehicle network at the moment, the unmanned aerial vehicle can move to the area and repair the bridge connection of the front node and the rear node, and the reachable state information of the unit moment is modified by the unmanned aerial vehicle according to the overlapped part;
if the accessible area of the unmanned aerial vehicle is not overlapped with the disconnection area of the vehicle network, the priority scores of the disconnection area of the network and the previous overlapped repairable disconnection area are required to be compared, if the priority of the disconnection area of the previous moment is high, the disconnection area repair of the current moment is abandoned, the backward calculation is continued, if the priority of the disconnection area of the network of the current moment is high, the disconnection area repair of the previous moment is abandoned, the accessible state of the current moment is recalculated according to the state of the unmanned aerial vehicle before the previous moment, and whether the disconnection area can be repaired is determined again;
representation method of reachable state of unmanned aerial vehicle at certain moment and based on t0The state of the time is calculated to the next time t1A calculation formula of the reachable state;
reachable status information includes
Figure FDA0002798026090000031
Wherein
Figure FDA0002798026090000032
And
Figure FDA0002798026090000033
is t0The left and right edge positions that can be reached at the moment,
Figure FDA0002798026090000034
the minimum speed achievable, i.e. the maximum speed in the left direction, is here uniformly expressed as a vector,
Figure FDA0002798026090000035
in order to be able to achieve the maximum speed,
Figure FDA0002798026090000036
for the maximum position where the unmanned aerial vehicle can reach the minimum speed, the same principle is adopted
Figure FDA0002798026090000037
The minimum position where the unmanned aerial vehicle can reach the maximum speed; the calculation formula of each state information is as follows:
Figure FDA0002798026090000038
Figure FDA0002798026090000039
Figure FDA00027980260900000310
Figure FDA00027980260900000311
Figure FDA00027980260900000312
Figure FDA00027980260900000313
wherein, tarFor unmanned aerial vehicle from t0Velocity of time of day
Figure FDA00027980260900000314
Accelerating to its maximum speed UvmaxRequired time, talFor unmanned aerial vehicle from
Figure FDA00027980260900000315
Accelerate to its minimum speed Uvmin(Uvmax＝-Uvmin) The required time, a, is scalar acceleration;
Figure FDA0002798026090000041
Figure FDA0002798026090000042
Figure FDA0002798026090000043
Figure FDA0002798026090000044
wherein, tdlAnd tdrRespectively subtracting the acceleration of the unmanned aerial vehicle to the minimum speedThe time required for the velocity and maximum speed, which is less than 0, means that the drone cannot accelerate to its physical maximum speed per unit time;
when the reachable area of the unmanned aerial vehicle overlaps with the disconnected area of the vehicle network, the reachable state information of the unmanned aerial vehicle is corrected
Figure FDA0002798026090000045
The calculation formula of (2):
Figure FDA0002798026090000051
Figure FDA0002798026090000052
Figure FDA0002798026090000053
Figure FDA0002798026090000054
Figure FDA0002798026090000055
Figure FDA0002798026090000056
wherein, tl1And tl2Two positive numerical solutions for t, for equation (18)r1And tr2Two positive numerical solutions for t for equation (20);
5) and finally, after all unit time in the next period is calculated, the unmanned aerial vehicle movement strategy in the period can be sequentially generated according to the time reverse order according to the reachable state updated in each unit time.
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