CN115171433A - Vehicle fog-assisted post-disaster rescue task unloading method for unmanned aerial vehicle - Google Patents
Vehicle fog-assisted post-disaster rescue task unloading method for unmanned aerial vehicle Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0043—Traffic management of multiple aircrafts from the ground
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0047—Navigation or guidance aids for a single aircraft
- G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0073—Surveillance aids
- G08G5/0086—Surveillance aids for monitoring terrain
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The invention discloses a vehicle fog-assisted post-disaster rescue task unloading method for an unmanned aerial vehicle, which comprises the following steps: establishing a multi-objective joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and computing resources of an aerial unmanned aerial vehicle; determining the data scale of the rescue task, and calculating the complexity and the maximum allowable time delay; thirdly, determining the number, computing resources and positions of rescue vehicles in the communication range of the unmanned aerial vehicle; calculating the communication rate between each vehicle through the unmanned aerial vehicle; determining the time delay and energy consumption of the executed task, and calculating the task distribution ratio through an evolutionary algorithm; and step five, dividing the rescue task into a plurality of subtasks according to the task distribution ratio, unloading the plurality of subtasks to the rescue vehicle for execution, and uploading the task result to the unmanned aerial vehicle after the rescue vehicle completes the task.
Description
Technical Field
The invention relates to a vehicle fog-assisted post-disaster rescue task unloading method for an unmanned aerial vehicle, and belongs to the field of unmanned aerial vehicle rescue.
Background
The occurrence of natural disasters often causes a great amount of casualties and serious economic losses, which become one of the important factors threatening the safety and stability of the modern society. Effective rescue after disaster can not only timely recover loss, but also provide further guarantee for life safety and social stability of people. Compare in traditional rescue mode, unmanned aerial vehicle's rapid development provides more effective and nimble mode for rescue after the calamity, plays important role in rescue after present calamity.
However, limited computing resources and battery capacity make it difficult for an unmanned person to meet low latency requirements and long-term work needs when performing rescue tasks for complex computations. Although the computing power and the battery capacity of the unmanned aerial vehicle are continuously improved, the new requirements of rescue tasks cannot be met. The advent of fog computing provides a new solution to providing low latency services to devices with insufficient computing resources. In a disaster area, rescue vehicles equipped with sufficient computing resources and energy can be used as fog nodes to provide fog computing for unmanned aerial vehicles.
Disclosure of Invention
The invention designs and develops a vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method, which divides a rescue task executed by an unmanned aerial vehicle into a plurality of subtasks by combining unmanned aerial vehicle and rescue vehicle task allocation and allocates the subtasks to the unmanned aerial vehicle and the rescue vehicle for common processing so as to enable the network to obtain the best performance.
The technical scheme provided by the invention is as follows:
a vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method comprises the following steps:
establishing a multi-objective joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and the computing capacity of the unmanned aerial vehicle in the air;
determining the data scale of the rescue task, and calculating the complexity and the maximum allowable time delay;
establishing a communication model, wherein the unmanned aerial vehicle is communicated with the ground vehicle to determine the number, the computing capacity and the position of the rescue vehicles in a communication range;
calculating the communication rate between the unmanned aerial vehicle and each vehicle through a communication model, determining the time delay and energy consumption of the executed task, and calculating the task distribution ratio through an evolutionary algorithm;
and step five, dividing the rescue task into a plurality of subtasks according to the task distribution ratio, unloading the plurality of subtasks to the rescue vehicle for execution, and uploading the task result to the unmanned aerial vehicle after the rescue vehicle completes the task.
Preferably, in the fourth step, the calculating of the communication rate includes:
calculating the distance from the unmanned aerial vehicle to all ground vehicles:
in the formula ,(xu ，y u ，z u) and (xm ，y m 0) coordinates representing the unmanned aerial vehicle and the ground vehicle, respectively;
calculate the average communication channel power gain with each vehicle:
in the formula ,
representing the line-of-sight communication probability, beta, between the drone and the vehicle 0 Represents the path loss of a reference distance of 1 meter under line-of-sight communication conditions, and k represents an additional attenuation factor due to non-line-of-sight communication;
calculate the average communication rate with each vehicle:
wherein B represents the bandwidth of the channel, P trans Representing the transmission power of the drone, σ 2 Representing the noise power.
Preferably, in the fifth step, the total time delay calculation formula of the rescue task is as follows:
wherein ,
T loc indicating the time delay, lambda, of the processing task of the drone 0 Representing the ratio of tasks allocated to the drone to the total task, eta representing the computational complexity of the task, D representing the data size of the total task, f u Representing computing resources of the drone;
indicating the time delay of the transfer of the task off-load to the m-th vehicle, lambda m Representing the ratio of tasks assigned to the m-th vehicle to the total task, f m Indicating the free computing resource, T, of the m-th vehicle m Indicating the task processing delay of the mth vehicle.
Preferably, in the fifth step, the total processing energy consumption calculation formula of the rescue task is as follows:
wherein ,
E loc representing the energy consumption of the processing task of the unmanned aerial vehicle, k representing the effective switch capacitance related to the CPU architecture, and omega being a constant; e m Indicating that the drone allocated the task to the m-th vehicleTransmission power consumption of P trans Representing the transmission power of the drone.
Preferably, in the fifth step, the total optimization objective function of the rescue task is as follows:
where α and β represent the weight of the time delay and energy consumption, respectively.
The invention has the following beneficial effects:
according to the vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method, the rescue task executed by the unmanned aerial vehicle is divided into a plurality of sub-tasks to be executed on the local unmanned aerial vehicle and the vehicle serving as the fog node respectively according to the task distribution ratio designed by the evolutionary algorithm, so that the time delay and the energy consumption of the unmanned aerial vehicle for independently processing the rescue task are effectively reduced, and the performance of the unmanned aerial vehicle in disaster rescue is improved. And the evolutionary algorithm combines the excellent global search capability of the genetic algorithm and the excellent local search capability of the invasive weed optimization algorithm, and has faster convergence compared with the traditional genetic algorithm and the invasive weed optimization algorithm.
Drawings
Fig. 1 is a schematic structural diagram of a vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading network.
Fig. 2 is a flow chart of the unmanned aerial vehicle and rescue task combined allocation method.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1-2, the invention provides a vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method,
establishing a multi-objective joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and the computing capacity of the unmanned aerial vehicle in the air;
determining the data scale of the rescue task, and calculating the complexity and the maximum allowable time delay;
the complexity is the number of CPU cycles required to be executed by each bit of data;
thirdly, the unmanned aerial vehicle communicates with the ground vehicle to determine the number, the computing capacity and the position of the rescue vehicles within the communication range;
step four, establishing a communication model, calculating the communication rate between the unmanned aerial vehicle and each vehicle through the communication model, determining the time delay and energy consumption of the executed task, and calculating the task distribution ratio through an evolutionary algorithm, wherein the method comprises the following steps of:
calculating the distance from the unmanned aerial vehicle to all ground vehicles:
in the formula ,(xu ，y u ，z u) and (xm ，y m 0) coordinates representing the unmanned aerial vehicle and the ground vehicle, respectively;
calculate the average communication channel power gain with each vehicle:
in the formula ,
representing the line-of-sight communication probability, beta, between the drone and the vehicle 0 Represents the path loss of a reference distance of 1 meter under the condition of line-of-sight communication, and k represents an additional attenuation factor caused by non-line-of-sight communication;
calculate the average communication rate with each vehicle:
wherein B represents the bandwidth of the channel, P trans Representing the transmission power of the drone, σ 2 Representation of noiseThe sound power;
designing time delay and energy consumption of executed tasks according to the optimization target, combining the time delay and the energy consumption into a system overhead function to convert a multi-target optimization problem into a single-target optimization problem, and designing a task distribution ratio by using an evolutionary algorithm; the unmanned aerial vehicle divides the rescue task into a plurality of subtasks according to the task distribution ratio, and the subtasks are unloaded to the rescue vehicle for execution; rescue vehicle uploads the result of task execution to unmanned aerial vehicle, includes:
the total time delay calculation formula of the rescue task is as follows:
wherein ,
T loc indicating the time delay, lambda, of the processing task of the drone 0 Representing the ratio of tasks allocated to the drone to the total task, eta representing the computational complexity of the task, D representing the data size of the total task, f u Representing computing resources of the drone;
indicating the time delay of the transfer of the task off-load to the m-th vehicle, lambda m Representing the ratio of tasks assigned to the m-th vehicle to the total task, f m Indicating the free computing resource, T, of the m-th vehicle m Representing the task processing time delay of the mth vehicle;
the total processing energy consumption calculation formula of the rescue task is as follows:
wherein ,
E loc representing the energy consumption of the processing task of the unmanned aerial vehicle, k representing the effective switch capacitance related to the CPU architecture, and omega being a constant; e m Representing transmission energy consumption, P, of the drone to assign the task to the m-th vehicle trans Representing the transmission power of the drone;
the overall optimization objective function of the rescue mission is:
where α and β represent the weight of the delay and energy consumption, respectively.
Based on the objective function, an evolutionary algorithm is used for designing an optimal task distribution ratio, and the specific process of the algorithm is as follows:
(1) Firstly, initializing a population with the size of N, wherein each individual in the population represents a candidate solution of an optimization objective function;
(2) Then calculating the fitness of each individual according to an optimized objective function, selecting the individual with the best fitness as an elite individual, selecting a pair of individuals from the rest individuals each time through roulette, and selecting the individual with higher fitness as a father until N fathers are selected;
(3) Selecting a pair of parents from the parent population each time, and generating a pair of offspring individuals through cross operation;
(4) Carrying out mutation operation on individuals of the offspring population according to a certain probability;
(5) Calculating the fitness of individuals in the filial generation population, and replacing the worst individual in the population by using an elite individual;
(6) Repeating (2) to (5) until an iteration termination condition is met;
(7) Calculating the number of seeds generated by each individual according to the fitness of the obtained population;
(8) Randomly dispersing the generated seeds around the parent individuals according to a normal distribution mode to grow new individuals;
(9) Selecting N individuals with highest fitness from the parent population and the offspring population to form a new population;
(10) And (7) repeating the steps (7) to (9) until an iteration termination condition is met, and finally outputting the individual with the highest fitness in the population, namely the optimal solution of the algorithm.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (5)
1. The vehicle fog assisted post-disaster rescue task unloading method for the unmanned aerial vehicle is characterized by comprising the following steps of:
establishing a multi-objective joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and the computing capacity of the unmanned aerial vehicle in the air;
determining the data scale of the rescue task, and calculating the complexity and the maximum allowable time delay;
thirdly, the unmanned aerial vehicle communicates with the ground vehicle to determine the number, the computing capacity and the position of the rescue vehicles within the communication range;
establishing a communication model, calculating the communication rate between the unmanned aerial vehicle and each vehicle through the communication model, determining the time delay and energy consumption of the executed task, and calculating the task distribution ratio through an evolutionary algorithm;
and step five, dividing the rescue task into a plurality of subtasks according to the task distribution ratio, unloading the plurality of subtasks to the rescue vehicle for execution, and uploading the task result to the unmanned aerial vehicle after the rescue vehicle completes the task.
2. The vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method according to claim 1, wherein in the fourth step, the calculation process of the communication rate comprises:
calculating the distance from the unmanned aerial vehicle to all ground vehicles:
in the formula ,(xu ，y u ，z u) and (xm ，y m 0) coordinates representing the unmanned aerial vehicle and the ground vehicle, respectively;
calculating the average communication channel power gain with each vehicle:
in the formula ,
representing the line-of-sight communication probability, beta, between the drone and the vehicle 0 Represents the path loss of a reference distance of 1 meter under line-of-sight communication conditions, and k represents an additional attenuation factor due to non-line-of-sight communication;
calculate the average communication rate with each vehicle:
wherein B represents the bandwidth of the channel, P trans Representing the transmission power, σ, of the drone 2 Representing the noise power.
3. The vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method according to claim 2, wherein in the fifth step, a total time delay calculation formula of the rescue task is as follows:
wherein ,
T loc indicating the time delay, lambda, of the unmanned aerial vehicle processing the task 0 Representing the ratio of tasks allocated to the drone to the total task, eta representing the computational complexity of the task, D representing the data size of the total task, f u Representing computing resources of the drone;
indicating the time delay of the transfer of the task off-load to the m-th vehicle, lambda m Representing the ratio of tasks assigned to the m-th vehicle to the total task, f m Indicating the free computing resource, T, of the m-th vehicle m Indicating the task processing delay of the mth vehicle.
4. The vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method according to claim 2, wherein in the fifth step, a calculation formula of total processing energy consumption of the rescue task is as follows:
wherein ,
E loc representing the energy consumption of the processing task of the unmanned aerial vehicle, k representing the effective switch capacitance related to the CPU architecture, and omega being a constant; e m Represents the transmission energy consumption of the unmanned aerial vehicle to assign the task to the m-th vehicle, P trans Representing the transmission power of the drone.
5. The vehicle fog-assisted unmanned aerial vehicle post-disaster rescue task unloading method according to claim 4, wherein in the fifth step, a total optimization objective function of the rescue task is as follows:
where α and β represent the weight of the time delay and energy consumption, respectively.
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