CN115171433B - Method for unloading post-disaster rescue task of fog-assisted unmanned aerial vehicle - Google Patents
Method for unloading post-disaster rescue task of fog-assisted unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a method for unloading post-disaster rescue tasks of a vehicle fog auxiliary unmanned aerial vehicle, which comprises the following steps: step one, establishing a multi-target 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 the unmanned aerial vehicle in the air; step two, determining the data scale of the rescue task, and calculating the complexity and the maximum allowable time delay; step three, determining the number, calculation 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 the energy consumption of the task to be executed, and calculating the task distribution ratio through an evolutionary algorithm; dividing the rescue task into a plurality of subtasks according to the task distribution ratio, unloading the subtasks to the rescue vehicle for execution, and uploading a task result to the unmanned aerial vehicle after the rescue vehicle finishes the task.
Description
Technical Field
The invention relates to a method for unloading a post-disaster rescue task of a vehicle fog auxiliary unmanned aerial vehicle, and belongs to the field of unmanned aerial vehicle rescue.
Background
The occurrence of natural disasters often causes a great deal of casualties and serious economic loss, and becomes one of important factors threatening the safety and stability of the current society. The effective post-disaster rescue not only can timely recover the loss, but also provides further guarantee for the safety and social stability of the people living in life. Compared with the traditional rescue mode, the rapid development of the unmanned aerial vehicle provides a more effective and flexible mode for post-disaster rescue, and plays an important role in current post-disaster rescue.
However, limited computing resources and battery capacity make it difficult for an unmanned person to meet low latency requirements and long-term operational needs in performing complex computational rescue tasks. Although the computing power and the battery capacity of the unmanned aerial vehicle are continuously improved, the new requirements of rescue tasks still cannot be met. The advent of fog computing has provided a new solution to providing low latency services to devices with inadequate computing resources. In a disaster area, a rescue vehicle equipped with sufficient computing resources and energy sources can serve as a fog node to provide fog computation for an unmanned aerial vehicle.
Disclosure of Invention
The invention designs and develops a vehicle fog auxiliary 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 to be distributed to the unmanned aerial vehicle and the rescue vehicle for common treatment by combining task distribution of the unmanned aerial vehicle and the rescue vehicle so as to ensure that the network obtains the best performance.
The technical scheme provided by the invention is as follows:
a method for unloading a post-disaster rescue task of a fog-assisted unmanned aerial vehicle comprises the following steps:
step one, establishing a multi-target joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and computing capacity of the unmanned aerial vehicle in the air;
step two, 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 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 the energy consumption of the task to be executed, and calculating the task distribution ratio through an evolutionary algorithm;
dividing the rescue task into a plurality of subtasks according to the task distribution ratio, unloading the subtasks to the rescue vehicle for execution, and uploading a task result to the unmanned aerial vehicle after the rescue vehicle finishes the task.
Preferably, in the fourth step, the calculating process of the communication rate includes:
calculating the distance between the unmanned aerial vehicle and all ground vehicles:
in the formula ,(xu ，y u ，z u) and (xm ，y m 0) representing the coordinates of the unmanned aerial vehicle and the ground vehicle, respectively;
calculating an average communication channel power gain with each vehicle:
in the formula ,representing the probability of line-of-sight communication between a drone and a vehicle, beta 0 Representing path loss for a reference distance of 1 meter under line-of-sight communication conditions, and κ represents an additional attenuation factor due to non-line-of-sight communication;
calculating an average communication rate with each vehicle:
wherein B represents the bandwidth of the channel, P trans Representing the transmission power, sigma, of an unmanned aerial vehicle 2 Representing noise power.
Preferably, in the fifth step, the total time delay calculation formula of the rescue task is:
wherein ,
T loc represents the time delay, lambda of unmanned aerial vehicle processing task 0 Representation ofThe ratio of the tasks allocated to the unmanned aerial vehicle to the total tasks, eta represents the computational complexity of the tasks, D represents the data size of the total tasks, f u Representing computing resources of the unmanned aerial vehicle;representing the transmission delay of the task offloaded to the mth vehicle lambda m Representing the ratio of the tasks allocated to the mth vehicle to the total tasks, f m Representing free computing resources of the mth vehicle, T 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:
wherein ,
E loc the energy consumption of unmanned plane processing tasks is represented, k represents an effective switch capacitance related to a CPU architecture, and ω is a constant; e (E) m Representing the transmission energy consumption of unmanned aerial vehicle for assigning tasks to the mth vehicle, P trans Representing the transmission power of the drone.
Preferably, in the fifth step, the overall optimization objective function of the rescue task is:
where α and β represent the weights of time delay and energy consumption, respectively.
The beneficial effects of the invention are as follows:
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 subtasks to be respectively executed on the unmanned aerial vehicle local and the vehicle serving as a fog node together 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 global searching capability of the excellent genetic algorithm and the local searching capability of the excellent 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 post-disaster rescue task unloading network of a vehicle fog-assisted unmanned aerial vehicle.
Fig. 2 is a flowchart of the method for allocating a united unmanned aerial vehicle and rescue tasks according to the present invention.
Detailed Description
The present invention is described in further detail below with reference to the drawings to enable those skilled in the art to practice the invention by referring to the description.
As shown in fig. 1-2, the invention provides a method for unloading post-disaster rescue tasks of a fog-assisted unmanned aerial vehicle,
step one, establishing a multi-target joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and computing capacity of the unmanned aerial vehicle in the air;
step two, determining the data scale of the rescue task, and calculating the complexity and the maximum allowable time delay;
wherein the complexity is the number of cpu cycles that need to be performed per bit of data;
thirdly, the unmanned aerial vehicle communicates with the ground vehicle to determine the number, the computing power and the position of rescue vehicles in a 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 the energy consumption of the task to be executed, and calculating the task distribution ratio through an evolutionary algorithm, wherein the method comprises the following steps:
calculating the distance between the unmanned aerial vehicle and all ground vehicles:
in the formula ,(xu ，y u ，z u) and (xm ，y m 0) representing the coordinates of the unmanned aerial vehicle and the ground vehicle, respectively;
calculating an average communication channel power gain with each vehicle:
in the formula ,representing the probability of line-of-sight communication between a drone and a vehicle, beta 0 Representing path loss for a reference distance of 1 meter under line-of-sight communication conditions, and κ represents an additional attenuation factor due to non-line-of-sight communication;
calculating an average communication rate with each vehicle:
wherein B represents the bandwidth of the channel, P trans Representing the transmission power, sigma, of an unmanned aerial vehicle 2 Representing noise power;
step five, firstly, designing time delay and energy consumption of a task to be executed according to an optimization target, then 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 finally, designing a task distribution ratio by using an evolutionary algorithm; dividing a rescue task into a plurality of subtasks according to the task distribution ratio by the unmanned aerial vehicle, and unloading the subtasks to a rescue vehicle for execution; the rescue vehicle uploads the result of task execution to the unmanned aerial vehicle, comprising:
the total time delay calculation formula of the rescue task is as follows:
wherein ,
T loc represents the time delay, lambda of unmanned aerial vehicle processing task 0 Represents the ratio of the tasks allocated to the unmanned aerial vehicle to the total tasks, η represents the computational complexity of the tasks, D represents the data size of the total tasks, f u Representing computing resources of the unmanned aerial vehicle;representing the transmission delay of the task offloaded to the mth vehicle lambda m Representing the ratio of the tasks allocated to the mth vehicle to the total tasks, f m Representing free computing resources of the mth vehicle, T m Representing a task processing delay of an mth vehicle;
the total processing energy consumption calculation formula of the rescue task is as follows:
wherein ,
E loc the energy consumption of unmanned plane processing tasks is represented, k represents an effective switch capacitance related to a CPU architecture, and ω is a constant; e (E) m Representing the transmission energy consumption of unmanned aerial vehicle for assigning tasks to the mth vehicle, P trans Representing the transmission power of the unmanned aerial vehicle;
the total optimization objective function of the rescue task is as follows:
where α and β represent the weights of time delay and energy consumption, respectively.
Based on the objective function, an optimal task allocation ratio is designed by using an evolutionary algorithm, and the specific process of the algorithm is as follows:
(1) Firstly, initializing a population with a scale 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 the optimization objective function, selecting the individual with the best fitness as elite individual, selecting a pair of individuals each time by roulette on the rest individuals, and selecting the individual with higher fitness as father body until N father bodies are selected;
(3) Selecting a pair of father bodies from the father body population each time, and generating a pair of child generation individuals through cross operation;
(4) Carrying out mutation operation on individuals of the offspring population with a certain probability;
(5) Calculating fitness of individuals in the offspring population, and replacing worst individuals in the population by elite individuals;
(6) Repeating (2) to (5) until an iteration termination condition is satisfied;
(7) Calculating the seed number generated by each individual according to the fitness of the obtained population;
(8) Randomly dispersing the generated seeds around the parent individuals in 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) Repeating the steps (7) to (9) until the iteration termination condition is met, wherein the individual with the highest fitness in the finally output population is the optimal solution of the algorithm.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (5)
1. The method for unloading the post-disaster rescue task of the unmanned aerial vehicle assisted by the vehicle fog is characterized by comprising the following steps of:
step one, establishing a multi-target joint optimization model for reducing time delay and energy consumption of an unmanned aerial vehicle to execute tasks, and determining the position and computing capacity of the unmanned aerial vehicle in the air;
step two, 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 power and the position of rescue vehicles in a 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 the energy consumption of the task to be executed, and calculating the task distribution ratio through an evolutionary algorithm;
dividing the rescue task into a plurality of subtasks according to the task distribution ratio, unloading the subtasks to the rescue vehicle for execution, and uploading a task result to the unmanned aerial vehicle after the rescue vehicle finishes the task.
2. The method for unloading post-disaster rescue tasks of the fog-assisted unmanned aerial vehicle according to claim 1, wherein in the fourth step, the calculation process of the communication rate comprises:
calculating the distance between the unmanned aerial vehicle and all ground vehicles:
in the formula ,(xu ，y u ，z u) and (xm ，y m 0) representing the coordinates of the unmanned aerial vehicle and the ground vehicle, respectively;
calculating an average communication channel power gain with each vehicle:
in the formula ,representing the probability of line-of-sight communication between a drone and a vehicle, beta 0 Representing path loss for a reference distance of 1 meter under line-of-sight communication conditions, and κ represents an additional attenuation factor due to non-line-of-sight communication;
calculating an average communication rate with each vehicle:
wherein B represents the bandwidth of the channel, P trans Representing the transmission power, sigma, of an unmanned aerial vehicle 2 Representing noise power.
3. The method for unloading post-disaster rescue tasks of the fog-assisted unmanned aerial vehicle according to claim 2, wherein in the fifth step, a total time delay calculation formula of the rescue tasks is as follows:
wherein ,
T loc represents the time delay, lambda of unmanned aerial vehicle processing task 0 Represents the ratio of the tasks allocated to the unmanned aerial vehicle to the total tasks, η represents the computational complexity of the tasks, D represents the data size of the total tasks, f u Representing computing resources of the unmanned aerial vehicle;representing the transmission delay of the task offloaded to the mth vehicle lambda m Representing the ratio of the tasks allocated to the mth vehicle to the total tasks, f m Representing free computing resources of the mth vehicle, T m Indicating the task processing delay of the mth vehicle.
4. The method for unloading post-disaster rescue tasks of the fog-assisted unmanned aerial vehicle according to claim 2, wherein in the fifth step, a total processing energy consumption calculation formula of the rescue tasks is as follows:
wherein ,
E loc the energy consumption of unmanned plane processing tasks is represented, k represents an effective switch capacitance related to a CPU architecture, and ω is a constant; e (E) m Representing the transmission energy consumption of unmanned aerial vehicle for assigning tasks to the mth vehicle, P trans Representing the transmission power of the drone.
5. The method for unloading post-disaster rescue mission of a vehicle fog-assisted unmanned aerial vehicle according to claim 4, wherein in the fifth step, a total optimization objective function of the rescue mission is:
where α and β represent the weights of time delay and energy consumption, respectively.
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