CN116301045B - Unmanned aerial vehicle data acquisition task allocation method oriented to space-time constraint network - Google Patents
Unmanned aerial vehicle data acquisition task allocation method oriented to space-time constraint network Download PDFInfo
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Abstract
The invention discloses a space-time constraint network-oriented unmanned aerial vehicle data acquisition task allocation method, which comprises the following steps: if the position of the task requester is in the working area of the unmanned aerial vehicle, acquiring a task set which can be executed by the unmanned aerial vehicle when the unmanned aerial vehicle is in an idle state and the task of the task requester is in an unexecuted state; distributing unmanned aerial vehicle data acquisition tasks according to the residual effective time of the tasks to obtain task distribution signals; the spatial crowdsourcing server sends a task allocation signal to the drone. Aiming at the requirements of unmanned aerial vehicle task allocation in space-time constraint network data acquisition, the invention considers the space-time requirements of different tasks and the requirements of unmanned aerial vehicle mobility on the allocation of the data acquisition tasks, fully considers the problem that unmanned aerial vehicles are inconvenient to supplement energy due to battery limitation, balances the data acquisition tasks of each unmanned aerial vehicle under the condition of limited battery capacity, and improves the overall efficiency of task allocation.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a space-time constraint network-oriented unmanned aerial vehicle data acquisition task allocation method.
Background
Under the promotion of continuous development of space-time constraint services such as resource exploration, reconnaissance monitoring, anti-diving, travel, transportation, emergency communication and the like, the unmanned aerial vehicle auxiliary space-time constraint data acquisition task is increasingly diversified, complicated and personalized. The provision of efficient and reliable data transmission and processing has gradually become one of the main subjects in the field of space-time constrained network research, and has promoted the development and application of network operation services based on the internet of things. In space-time constraint networks, network defects such as low data transmission rate, limited coverage, insufficient flexibility and the like bring great challenges to the execution of data acquisition tasks. Therefore, for different requirements of different space-time constraint business scenarios, it is necessary to formulate an effective data acquisition task allocation scheme.
By analyzing the space-time constraint network business requirements, there have been many efforts directed at data acquisition. In different business scenarios, there are different requirements on mobility, bandwidth, delay, energy consumption, etc. of data acquisition. For example, in user-oriented positioning, navigation and emergency communication scenarios, the efficiency of information transmission is prioritized, whereas in user-oriented voice and video transmission scenarios, transmission of large amounts of data is considered. The space-time requirements of different tasks and the requirements of unmanned aerial vehicle mobility on data acquisition task allocation are not comprehensively considered in the prior art, and are often factors limiting performance in practical application. And the conventional task allocation in land-based environments is mostly researched in the existing space crowdsourcing research content, and the space crowdsourcing task allocation in the environment with more space-time constraints is researched by less work. The space-time constraint of the marine environment is more, the number of unmanned aerial vehicles in the space-time constraint service scene is less, and the energy source is inconvenient to supplement. Therefore, under the condition of limited battery capacity, how to balance the data acquisition tasks of each unmanned aerial vehicle and improve the overall utilization rate of the unmanned aerial vehicle become new key challenges.
Disclosure of Invention
The invention provides a space-time constraint network-oriented unmanned aerial vehicle data acquisition task allocation method, which aims to overcome the technical problems.
A space-time constraint network-oriented unmanned aerial vehicle data acquisition task allocation method comprises the following steps:
s1: establishing a space-time constraint data acquisition network, wherein the space-time constraint data acquisition network comprises an unmanned aerial vehicle set U= { U formed by m unmanned aerial vehicles 1 ,u 2 ,…,u i ,…,u m Task set s= { S composed of n task requesters } 1 ,s 2 ,…,s j ,…,s n -and a spatial crowdsourcing server;
s2: acquiring position coordinates of an ith unmanned aerial vehicle, real-time energy of the ith unmanned aerial vehicle, position coordinates of a jth (j=1, 2, …, n) task requester, transmitting power of the jth task requester and remaining effective time of the jth task;
s3: according to the position coordinates of the ith unmanned aerial vehicle and the position coordinates of the jth task requester, acquiring the distance d between the ith unmanned aerial vehicle and the jth task requester ij The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the position of the jth task requester is in the working area of the ith unmanned aerial vehicle;
s4: if the position of the jth task requester is in the working area of the ith unmanned aerial vehicle, acquiring the current state of the ith unmanned aerial vehicle and the task state of the jth task requester;
s5: when the ith unmanned aerial vehicle is in an idle state currently and the task of the jth task requester is in an unexecuted state, executing S6;
s6: according to the transmitting power of the jth task requester, acquiring the signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the task of the jth task requester and the energy consumption of the ith unmanned aerial vehicle when executing the task of the jth task requester;
s7: determining whether the ith unmanned aerial vehicle can execute the task of the jth task requester, and acquiring a task set which can be executed by the ith unmanned aerial vehicle; distributing unmanned aerial vehicle data acquisition tasks according to the residual effective time of the tasks to obtain task distribution signals;
s8: and the space crowdsourcing server sends a task distribution signal to the unmanned aerial vehicle, so that the unmanned aerial vehicle completes the data acquisition task of the task requester.
The beneficial effects are that: the unmanned aerial vehicle data acquisition task allocation method for the space-time constraint network is mainly used for allocating unmanned aerial vehicle tasks in space-time constraint network data acquisition, takes space and time constraints of the tasks, mobility of the unmanned aerial vehicle and battery limitations into consideration, allocates the unmanned aerial vehicle data acquisition tasks, takes space-time requirements of different tasks and mobility of the unmanned aerial vehicle into consideration, fully considers the problem that the unmanned aerial vehicle is inconvenient to supplement energy due to battery limitations, balances the data acquisition tasks of each unmanned aerial vehicle under the condition of limited battery capacity, and improves overall task allocation efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a space crowdsourcing unmanned aerial vehicle data acquisition task allocation method facing a space-time constraint network in an embodiment of the invention;
FIG. 2 is a diagram of a data acquisition network under a space-time constraint in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of a space crowdsourcing data acquisition task allocation process based on a space-time constraint network in accordance with an embodiment of the present invention;
fig. 4 is a diagram of unmanned aerial vehicle and task distribution map in an electronic chart according to an embodiment of the present invention;
FIG. 5 is a task allocation diagram of space-time constrained network data acquisition in scenario 1 according to an embodiment of the present invention;
FIG. 6 is a task allocation diagram of space-time constrained network data acquisition in scenario 2 according to an embodiment of the present invention;
fig. 7 is a schematic diagram of task completion time of an unmanned aerial vehicle in scenario 1 according to an embodiment of the present invention;
fig. 8 is a schematic diagram of task completion time of an unmanned aerial vehicle in scenario 2 according to an embodiment of the present invention;
fig. 9 is a time distribution diagram of a task performed by a drone in scenario 1 according to an embodiment of the present invention;
fig. 10 is a time distribution diagram of a task performed by a drone in scenario 2 according to an embodiment of the present invention;
FIG. 11 is an energy diagram of an unmanned aerial vehicle in scenario 1 of an embodiment of the present invention;
fig. 12 is an energy diagram of an unmanned aerial vehicle in scenario 2 of an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The invention discloses a space-time constraint network-oriented unmanned aerial vehicle data acquisition task allocation method, which comprises the following steps of. As shown in fig. 1:
s1: establishing a space-time constraint data acquisition network, wherein the space-time constraint data acquisition network comprises an unmanned aerial vehicle set U= { U formed by m unmanned aerial vehicles 1 ,u 2 ,…,u i ,…,u m Task set s= { S composed of n task requesters } 1 ,s 2 ,…,s j ,…,s n -and a spatial crowdsourcing server;
in this embodiment, the network command center dispatches a certain number of unmanned aerial vehicles, unmanned ships, unmanned vehicles and other intelligent agents and mobile workstations to perform data acquisition tasks, monitors the working area and completes the data acquisition tasks. The unmanned aerial vehicle is a task executor for executing data acquisition tasks in the space-time constraint data acquisition network, corresponds to staff in space crowdsourcing, and executes specific data acquisition tasks according to the received instructions to the designated positions. Because the unmanned aerial vehicle has a large capacity, data is not unloaded in the task execution, and after the task execution is finished, the data is uniformly unloaded by the space crowdsourcing server. The unmanned ship, the navigation mark and the sensor are task requesters, and correspond to the task requesters in the space crowdsourcing, in daily operation, important data such as water temperature, weather, traffic flow and the like are collected, and generated information such as voice, image, video and the like waits for unmanned aerial vehicle collection. The navigation mark and the sensor are devices which are deployed in advance. The unmanned ship may act as a relay node in addition to gathering data. In addition, the mobile workstation corresponds to the space crowdsourcing server and distributes data acquisition tasks for the unmanned aerial vehicle. The unmanned aerial vehicle resource is utilized to the maximum extent, and the improvement of the task execution efficiency is of great importance. The mobile workstation not only has mobility and can receive information and send instructions in a mobile mode, but also can assign the unmanned aerial vehicle to execute data acquisition tasks according to task allocation instructions.
In particular, since the data acquisition tasks based on the space-time constrained network are time sensitive, the unmanned aerial vehicle is required to execute at a designated location. Thus, the maneuverability of the drone and the spatiotemporal nature of the tasks provide a basis for the allocation of data acquisition tasks. The data acquisition task allocation mode based on the space-time constraint network is that a space crowdsourcing server (mobile workstation) allocates tasks. The mobile workstation collects position information, power and energy consumption of the unmanned aerial vehicle, task requirements of task requesters such as data positions to be collected, data amount to be collected, task expiration time and the like, and distributes proper tasks to the unmanned aerial vehicle.
S2: acquiring position coordinates of an ith (i=1, 2, …, m) unmanned aerial vehicle, real-time energy of the ith unmanned aerial vehicle, position coordinates of a jth (j=1, 2, …, n) task requester, transmitting power of the jth task requester and remaining effective time of the jth task;
specifically, the task requester broadcasts a task to the mobile station. The tasks contain information such as geographical location, amount of information to be transmitted, and expiration time (remaining effective time). The task requester may upload information to the mobile station via a direct transmission or a relay transmission. And meanwhile, the unmanned aerial vehicle uploads information such as geographic position, power, energy and the like to the mobile workstation. The drone may upload information to the mobile workstation from a stationary location via direct or relay transmission.
S3: acquiring the distance d between the ith unmanned aerial vehicle and the jth task requester according to the position coordinates of the ith unmanned aerial vehicle (i=1, 2, …, m) and the position coordinates of the jth task requester (j=1, 2, …, n) ij The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the position of the jth task requester is in the working area of the ith unmanned aerial vehicle;
preferably, in the step S3, the method for determining whether the position of the jth task requester is in the working area of the ith unmanned aerial vehicle is as follows:
if it is
The j-th task requester is located in the working area of the i-th unmanned aerial vehicle, and the task is covered by the unmanned aerial vehicle and is recorded as
Wherein,
wherein x is i An x-axis coordinate of a real-time position for the ith (i=1, 2, …, m) frame of the drone; y is i The y-axis coordinate of the real-time position of the ith unmanned aerial vehicle; d, d ij Distance from the jth task requester to the ith unmanned plane position; x is x j X-axis coordinates for the j (j=1, 2, …, n) th task requester; y is j Y-axis coordinates for the jth task requester; i is the number of the unmanned aerial vehicle, and m is the total amount of the unmanned aerial vehicle; j is the number of the task requesters, n is the total number of the task requesters; x is x i The x-axis coordinate of the position of the ith unmanned aerial vehicle; y is i The y-axis coordinate of the position of the ith unmanned aerial vehicle;radius of coverage area of ith unmanned aerial vehicle;
specifically, as shown in fig. 2, the present embodiment establishes a data acquisition network scene (marine data acquisition scene) under a space-time constraint in combination with a space-time constraint data acquisition network. For complex space-time constraint business, the network command center manages the space-time constraint data acquisition network and provides service. Before the space-time constraint data acquisition task, the network command center dispatches a certain number of unmanned aerial vehicles, unmanned vessels and other intelligent bodies and mobile workstations to execute the data acquisition task and monitor the working area. After receiving the space-time constraint data acquisition task, the network command center performs task allocation. The coordinate system in this embodiment adopts the geodetic coordinate system, and for the requesters of the j (j=1, 2, …, n) th task, h is due to the distribution of tasks at the sea surface j ＝0,h j Is the height of the jth task requester.
S4: if the position of the jth task requester is in the working area of the ith unmanned aerial vehicle, namely m ij When the number is=1, the current state of the ith unmanned aerial vehicle and the task state of the jth task requester are obtained;
s5: when the ith unmanned aerial vehicle exists and is currently in an idle state, namely s 1i =0, and the task of the jth task requester is in the unexecuted state s 1j When=0, S6 is performed;
specifically, a task matching set M of the ith unmanned aerial vehicle is obtained task ＝[m ij ] M×N When the ith unmanned aerial vehicle matches the jth task, namely the position of the jth task requester is in the working area of the ith unmanned aerial vehicle, m ij =1, otherwise m ij =0, where m ij Representing the matching state of the ith unmanned aerial vehicle and the jth task requester, when m ij When the system is in the condition of being=1, the ith unmanned aerial vehicle is matched with the task of the jth task requester, namely the position of the jth task requester is in the working area of the ith unmanned aerial vehicle; when m is ij When the task is=0, the ith unmanned aerial vehicle is not matched with the task of the jth task requester;
the current state set of the ith unmanned aerial vehicle is acquired as S UAV ＝[s 1i ] 1×M When the ith unmanned aerial vehicle does not execute the task and is in the idle state currently, s 1i =0. Otherwise, the system is in busy state s 1i =1, at which time the ith drone is performing the task. Likewise, the current state set of the task is set to S task ＝[s 1j ] 1×N . When the j-th task is in the non-executing state, s 1j =0. Otherwise, it will be in the executed state s 1j ＝1。
S6: according to the transmitting power of the jth task requester, the signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the task of the jth task requester (i.e. the signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the task of the jth task requester) and the energy consumption of the ith unmanned aerial vehicle when executing the task of the jth task requester are obtained;
the signal-to-interference-and-noise ratio when the ith unmanned aerial vehicle executes the task of the jth task requester is obtained as follows:
in particular, since the channel condition between the unmanned aerial vehicle and the task requester greatly affects the information transmission quality, this relates to the task completion quality. Therefore, in the execution process of the jth task, the signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when collecting data needs to be calculated, which can be expressed as:
wherein SINR ij The signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the jth task is set; p (P) j The transmit power for the j-th task requester; lambda is the wavelength; g is the antenna direction coefficient of the task requester; i is interference when other unmanned aerial vehicles collect data; sigma (sigma) 2 The noise power of the unmanned aerial vehicle;is the path loss; />Representing the channel power gain at a single reference distance d=1m; d, d ij Representing the distance from the jth task requester to the ith unmanned aerial vehicle, namely the ith unmanned aerial vehicle to the jth unmanned aerial vehicleCommunication distance of task requester;
the energy consumption of the ith unmanned aerial vehicle for executing the task of the jth task requester is obtained as follows:
as the energy at which the drone performs the task is limited. The energy consumption of the ith unmanned plane for executing the jth task is flight energy consumptionEnergy consumption for hovering->And information transmission energy consumption->The sum can be expressed as:
wherein E is ij Executing the energy consumption of the task of the jth task requester for the ith unmanned aerial vehicle;is flight energy consumption;is hovering energy consumption; />The energy consumption for information transmission; p (P) i fly The flight power of the ith unmanned aerial vehicle; p (P) i hover The power of the spiral of the ith unmanned aerial vehicle is calculated; p (P) i commu The communication power of the ith unmanned aerial vehicle; />The flight time of the ith unmanned aerial vehicle;is the ith frame withoutThe hover time of the human machine; />The communication time of the ith unmanned aerial vehicle;
s7: determining whether the ith unmanned aerial vehicle can execute the task of the jth task requester, and acquiring a task set which can be executed by the ith unmanned aerial vehicle; distributing unmanned aerial vehicle data acquisition tasks according to the residual effective time of the tasks to obtain task distribution signals; as shown in fig. 3;
preferably, the task method for determining whether the ith unmanned aerial vehicle can execute the jth task requester is as follows:
if SINR ij ≥SINR th And is also provided withThe ith unmanned aerial vehicle can execute the task of the jth task requester;
wherein SINR ij The signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the jth task is set; SINR (Signal to interference plus noise ratio) th Is a signal-to-interference-and-noise ratio threshold; e (E) ij The energy consumption for executing the j-th task for the i-th unmanned aerial vehicle; e (E) th Is an unmanned energy consumption threshold;
the unmanned aerial vehicle needs to return after data are collected at a designated place, and the energy consumption of the ith unmanned aerial vehicle after the ith unmanned aerial vehicle finishes the j task return is expressed as:
wherein E is i Real-time energy of the ith unmanned aerial vehicle;the return energy consumption of the ith unmanned aerial vehicle; />The return energy consumption of the j-th task is executed for the i-th unmanned aerial vehicle; />Is the return time of the ith unmanned aerial vehicle, v i The flight speed of the ith unmanned aerial vehicle; />The return distance of the ith unmanned aerial vehicle; />The coordinate of the x axis of the initial position of the ith (i is more than or equal to 1 and less than or equal to m) frame unmanned aerial vehicle;and the y-axis coordinate of the initial position of the ith unmanned aerial vehicle.
The above conditions indicate that when the jth task request program is covered by the ith unmanned aerial vehicle and the signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle is not less than the signal-to-interference-and-noise ratio threshold when executing the task of the jth task requester, the signal-to-interference-and-noise ratio threshold SINR is set th And when the energy consumption of the ith unmanned aerial vehicle for executing the jth task is not more than the energy consumption threshold of the unmanned aerial vehicle, the ith unmanned aerial vehicle is distributed to the jth task.
Preferably, the method for distributing the unmanned aerial vehicle data acquisition task is as follows:
s71: according to whether the ith unmanned aerial vehicle can execute the task of the jth task requester, acquiring a task vector which can be executed by the ith unmanned aerial vehicle
Wherein S is i A task vector representing the i-th unmanned aerial vehicle being able to execute;representing the ith task that the ith unmanned aerial vehicle is capable of performing; p represents the number of tasks that the ith unmanned can perform; p represents the total number of tasks that the ith unmanned can perform;
s72: when p=1, the number of the active groups,the i-th unmanned can only execute task at the moment +.>Thus task->Is executed by the ith unmanned aerial vehicle, namely the final execution task of the ith unmanned aerial vehicle is +.>
S73: when P>1, the time is 1; according to the task set vector which can be executed by the ith unmanned aerial vehicle, a task vector set { S ] which can be executed by all unmanned aerial vehicles is obtained 1 ,S 2 ,...,S i ,...,S m -a }; to obtain and be able to performUnmanned aerial vehicle vector of (a)Wherein (1)>Can perform +.>Is an unmanned aerial vehicle; q is capable of executing task->Is a total number of unmanned aerial vehicles;
if the task is executedWhen there is no Q>1-> Any one of the tasks->The method comprises the steps that the method can only be executed by an ith unmanned aerial vehicle, and then a task finally executed by the ith unmanned aerial vehicle is obtained through a quality evaluation algorithm;
if Q is present>1, then There is a task->Can be performed by a Q-frame unmanned aerial vehicle>
Then pair Each task of->Executing reverse auction algorithm to determine task->Is preferably performed by (a)Unmanned plane->
If there is no p-th task that the i-th unmanned aerial vehicle can executeIs preferably performed unmanned aerial vehicle->The z-th task which can be performed with the i-th unmanned aerial vehicle +.>Is preferably performed unmanned aerial vehicle->When the same, then the ith task that the ith unmanned aerial vehicle can perform +.>For said preferred execution unmanned aerial vehicle +.>Is executed in the first place; wherein,
if there is the ith task that the ith unmanned aerial vehicle can executeIs preferably performed unmanned aerial vehicle->The z-th task which can be performed with the i-th unmanned aerial vehicle +.>Is preferably performed unmanned aerial vehicle->In the same case, i.e.)>I.e.At the same time->And->Is to obtain +.>Is executed in the first place;
specifically, obtain respectivelyExecution->And->The energy required during the process is selected as the task with small required energyIs executed in the first place;
s74: if presentNot belong to-> When the preferred execution of any one of the tasks is unmanned aerial vehicle,
if already presentAs a final execution task for the ith unmanned aerial vehicle or preferably for the execution of unmanned aerial vehicle +.>According to +.>Acquisition of ∈10 by quality assessment algorithm>Is executed in the first place;
otherwise according to S= { S 1 ,s 2 ,…,s j ,…,s n Acquisition by quality assessment algorithmIs executed in the first place.
Specifically, in the space-time constraint network data acquisition task allocation, the unmanned aerial vehicle is used as a data acquisition platform to acquire data collected by various intelligent agents. The drone has no specific requirements on the order of tasks. Nevertheless, tasks issued by task requesters are time sensitive in that they need to return data to the hub as soon as possible within the validity period. Aiming at the problem of lack of target consistency between the unmanned aerial vehicle and the task requester, a task completion quality assessment algorithm is designed to ensure the task completion quality of data acquisition.
Preferably, the method for obtaining the task finally executed by the ith unmanned aerial vehicle through a quality evaluation algorithm comprises the following steps:
if the ith unmanned aerial vehicle executes the p-th taskThe energy expended is +.>The i-th unmanned aerial vehicle finally executes the task as a task +.>
Wherein E is ip The energy consumption of the p-th task which can be executed by the i-th unmanned aerial vehicle.
According to the quality evaluation algorithm, the mobile workstation distributes corresponding tasks for the unmanned aerial vehicle which is currently idle, so that the unmanned aerial vehicle can be effectively executed. However, it is possible with this algorithm to assign a task to multiple drones. The present invention thus designs a reverse auction algorithm such that when there are multiple drones available to perform a task, the drone that minimizes task latency is selected for execution. The proper rewards are beneficial to the optimal allocation of tasks and the effective completion of data acquisition tasks. A low prize may reduce the work enthusiasm and task completion efficiency of the drone. However, excessive rewards not only impair the amount of task completion, but also reduce the overall efficiency of the mobile workstation. Therefore, in order to improve the enthusiasm of the unmanned aerial vehicle for completing tasks and reduce task waiting time, a reverse auction algorithm is designed to further perform space-time constraint network data acquisition task allocation. Reverse auction algorithms are task-centric, with task latency being the auction term. The mobile workstation acts as an auctioneer and the drone acts as a buyer to which to submit bids, wherein the submitted bids are task waiting times.
Preferably, the ith task that the ith unmanned aerial vehicle is capable of performing is determined according to a reverse auction algorithmIs preferably performed unmanned aerial vehicle->The method comprises the following steps:
acquisition ofTask waiting time of the q-th unmanned aerial vehicle in (2)>
In particular, since a task is time-efficient, it needs to be executed during the validity period. The waiting time of the task is the waiting time of the task requester when the task is executed, and on the basis, the waiting time of the task is shortened as much as possible, so that the task completion effect of space crowdsourcing can be improved. Thus obtaining the p-th task that the i-th unmanned aerial vehicle can performTask waiting time of the q-th unmanned aerial vehicle in the unmanned aerial vehicle set +.>
When (when)In the time-course of which the first and second contact surfaces,
if it meets the requirement, whenUnmanned aerial vehicle->The time for executing the task is
The ith task that the ith unmanned aerial vehicle is able to performIs preferably performed unmanned aerial vehicle->
Wherein,to be able to perform the ith task that the ith unmanned aerial vehicle is able to perform +.>Task waiting time of the q-th unmanned aerial vehicle in the unmanned aerial vehicle set; />Is a latency threshold;
wherein,t p remaining active time for the p-th task; />The task transmission time for the p-th task.
S8: and the space crowdsourcing server sends a task distribution signal to the unmanned aerial vehicle, so that the unmanned aerial vehicle completes the data acquisition task of the task requester.
In this embodiment, the mobile workstation allocates tasks to the unmanned aerial vehicle according to the task allocation signal. Specifically, the mobile workstation analyzes all collected data acquisition tasks and the spatial and temporal information of the unmanned aerial vehicle, but does not disclose task information to the unmanned aerial vehicle. In addition, the mobile workstation calculates the energy consumption, task waiting time and the like of the unmanned aerial vehicle according to the physical distance between the unmanned aerial vehicle and the task requester and the channel condition, and distributes corresponding tasks for the unmanned aerial vehicle. The mobile station sends instructions to the drone via direct or relayed transmissions. After the unmanned aerial vehicle performs the complete part task, the unmanned aerial vehicle needs to return to a starting point to unload data. Due to the limited energy, the drone cannot be far from the original location while performing the task.
Specifically, in a certain period of time, one unmanned aerial vehicle can only process one task. If a drone is assigned to a number of suitable tasks, then the drone selects the task with the least energy consumption. Other tasks are assigned to other idle drones at this time or waiting for the next time period to reassign. If a task is assigned to multiple drones, then the task is assigned to a drone with little task latency. The quality assessment algorithm and the reverse auction algorithm in this embodiment are alternately repeated until all currently idle drones are matched to the appropriate mission. If the drone does not have the proper mission in the coverage area, it is returned.
The application effect of the present invention will be described in detail with reference to simulation.
1. Simulation conditions
In the simulation scenario, the range is 18×8nmile 2 The scene is selected from a portion of the offshore area according to the electronic chart. The mission location and the drone location are shown in figure 4. Both green triangles and yellow triangles represent the position of the ship. According to the real-time positions of the ship, the beacon and the channel, the tasks are mainly distributed along the channel, and the data transmission time is randomly generated according to the sceneMeanwhile, unmanned aerial vehicles which are randomly deployed above the selected area execute tasks at the same flying height and speed. Let task number n=50, unmanned aerial vehicle quantity m=4, unmanned aerial vehicle flight height h i =20m, unmanned aerial vehicle flight speed v i =60 m/s. Setting the flying power and the hovering power of the unmanned aerial vehicle as P i fly =87w and P i hover =80w. Compared with flight energy consumption and hover energy consumption of the unmanned aerial vehicle, communication energy consumption of the unmanned aerial vehicle is set to be 10mW, and the communication energy consumption is too small to be ignored.
Aiming at two typical space-time constraint service scenes, a designed space crowdsourcing data acquisition task allocation heuristic (SC-MDC-TA) algorithm based on a space-time constraint network is adopted for task allocation. Scenario 1 is a user location navigation and emergency communication. Generally, such application scenario transmissions have a short packet size and a relatively low data rate. Therefore, data traffic of such application scenarios needs to be transmitted immediately. And the second scene is that the user transmits voice and video. Such an application scenario requires throughput up to Gbps.
2. Simulation content and result analysis
The performance of the proposed algorithm is demonstrated by comparison with other transmission methods.
Comparison algorithm-based on the closest data acquisition task assignment (CD-MDC-TA) algorithm: the currently idle drone is assigned the nearest unexecuted task. The CD-MDC-TA algorithm can achieve the shortest task execution time and the smallest energy consumption of the unmanned aerial vehicle at present, but cannot guarantee the shortest task time and the smallest energy consumption of the unmanned aerial vehicle after all tasks are completed.
Simulation 1: and analyzing the result of task allocation by adopting the SC-MDC-TA algorithm.
The data transmission requirement of the scene 1 is smaller, and the unmanned aerial vehicle can collect data in the flight process instead of hovering over a task. In contrast, due to the high requirements of scene 2 for voice and video transmissions, the drone needs to hover over the mission for data acquisition. According to the data size and the data processing capacity of the unmanned aerial vehicle, the hovering time of the unmanned aerial vehicle above the task is set to be a random number between 0 and 100.
Fig. 5 and 6 show the results of task allocation by the mobile station using SC-MDC-TA algorithm in scenario 1 and scenario 2. The straight line represents the flight path of the unmanned aerial vehicle, and the arrow represents the flight direction of the unmanned aerial vehicle. By comparing the results in different scenes, the influence of different information transmission amounts on task allocation can be seen. The unmanned aerial vehicle data acquisition task distribution method considers the diversity and individuation of tasks, and the mobile workstation calculates and distributes according to the task information. Therefore, the method can distribute data acquisition tasks according to different space-time constraint network business requirements, and has good adaptability to the space-time constraint network.
Simulation 2: and performing task allocation by adopting an SC-MDC-TA algorithm and a CD-MDC-TA algorithm, and comparing the task completion time of the unmanned aerial vehicle.
As can be seen from fig. 7, the unmanned aerial vehicle task completion time fluctuates between 672 and 1019 seconds with an average value of about 806s using the CD-MDC-TA algorithm. In contrast, unmanned aerial vehicle task completion times using the SC-MDC-TA algorithm can be seen to be distributed between 634s and 813s, with an average value of about 722s. Therefore, the completion time of the unmanned aerial vehicle task can be shortened by 84s, namely, 10 percent by adopting the SC-MDC-TA algorithm. As can be seen from fig. 8, the unmanned aerial vehicle task completion time ranges from 1334s to 1787s and the average value is about 1554s by using the CD-MDC-TA algorithm. In contrast, unmanned aerial vehicle mission completion times employing the SC-MDC-TA algorithm can be seen to fluctuate between 1132 and 1457s, with an average value of approximately 1334s. Therefore, the completion time of the unmanned aerial vehicle task can be shortened by 220s, namely, 14 percent by adopting the SC-MDC-TA algorithm.
The SC-MDC-TA algorithm evaluates the task completion quality and shortens the task waiting time. The task completion capacity of the unmanned aerial vehicle is considered through a task completion quality evaluation algorithm, and is not limited to the current optimization, so that the overall effectiveness of space-time constraint network data acquisition task distribution is optimized. The mobile workstation selects the unmanned aerial vehicle in a reverse auction algorithm mode, so that task waiting time is shortened, and task execution within a valid period is ensured. The above factors are critical to reducing task completion time. If the mobile workstation does not take into account the unmanned aerial vehicle's task completion capabilities, the unmanned aerial vehicle's aggressiveness in performing tasks will be lower and lower. If the mobile workstation does not consider task latency, the time to complete the task for the space-time constrained network data acquisition will increase. Therefore, the SC-MDC-TA algorithm is adopted, so that the task completion time of the unmanned aerial vehicle can be obviously shortened.
Simulation 3: and performing task allocation by adopting an SC-MDC-TA algorithm and a CD-MDC-TA algorithm, and comparing task completion time distribution of the unmanned aerial vehicle.
Fig. 9 and 10 show in box charts the unmanned aerial vehicle task completion time distribution using the CD-MDC-TA algorithm and the SC-MDC-TA algorithm, clearly showing the median, maximum, minimum, first and third quartiles of unmanned aerial vehicle task completion time. In addition, we can see that the data using the SC-MDC-TA algorithm is more focused. Therefore, the SC-MDC-TA algorithm is adopted, so that not only is the shorter unmanned aerial vehicle task completion time obtained, but also the time variance is smaller. This is because the SC-MDC-TA algorithm takes into account the overall performance of the task completion, by limiting the coverage of the drone, it is ensured that the drone is not too far from the initial location. Therefore, although the two algorithms can realize data acquisition task allocation adapting to space-time constraint business scenes, the SC-MDC-TA algorithm is more advantageous. Therefore, the SC-MDC-TA algorithm not only shortens the task completion time of the unmanned aerial vehicle, but also realizes better task balance.
Simulation 4: and adopting different algorithms to perform task allocation, and comparing the energy consumption of the unmanned aerial vehicle.
Fig. 11 shows the unmanned energy consumption situation in scenario 1. The energy consumption range of the unmanned aerial vehicle is 58 kJ-88 kJ by adopting a CD-MDC-TA algorithm, and the average value is about 70kJ. In contrast, unmanned energy consumption using the SC-MDC-TA algorithm can be seen to fluctuate between 54kJ and 71kJ, with an average value of about 63kJ. It can be found that the adoption of the SC-MDC-TA algorithm can remarkably reduce the energy consumption of the unmanned aerial vehicle by 7kJ, namely 10 percent. Similarly, fig. 12 shows the unmanned energy consumption situation in scenario 2. Unmanned energy consumption using the CD-MDC-TA algorithm was distributed between 115kJ and 154kJ with an average value of about 135kJ. In contrast, with the SC-MDC-TA algorithm, the energy consumption of the drone fluctuates between 98kJ and 126kJ, with an average value of about 115kJ. It can be found that the adoption of the SC-MDC-TA algorithm can reduce the energy consumption of the unmanned aerial vehicle for executing tasks by 20kJ, namely by 15%.
The task completion quality assessment algorithm plays a very important role in the SC-MDC-TA algorithm. The mobile workstation calculates and compares the energy consumption of the unmanned aerial vehicle in space-time constraint network data acquisition task allocation. The energy consumption of the unmanned aerial vehicle is an important index for evaluating the completion quality of the space-time constraint network data acquisition task. This ensures a minimum energy consumption of the drone during the execution of the task. In addition, the drone performs tasks as close to its initial location as possible, depending on the coverage. Therefore, the method can ensure that the minimum return energy consumption of the unmanned aerial vehicle after completing all tasks is ensured. It can be seen that the method provides energy support for the drone from departure to return. Therefore, the SC-MDC-TA algorithm can obviously reduce the energy consumption of the unmanned aerial vehicle.
The invention mainly aims at the unmanned aerial vehicle task allocation requirement in space-time constraint network data acquisition, and considers two decisive factors to optimize task allocation performance: 1) Space and time constraints of the data acquisition task, 2) unmanned aerial vehicle maneuverability and battery limitations. In addition, the space-time constraint network is generally harsh in environment, and the fixed wing unmanned aerial vehicle can better resist interference. Modeling the task allocation problem of the unmanned aerial vehicle for space-time constraint network data acquisition, and optimizing by an algorithm, wherein the signal-to-interference-and-noise ratio, the energy consumption of the unmanned aerial vehicle and the task waiting time are considered, so that the unmanned aerial vehicle can be allocated to a proper data acquisition task while the task can be completed in the shortest time. According to the allocation method, the task completion quality assessment algorithm and the reverse auction algorithm are integrated to optimize the data acquisition task allocation of the unmanned aerial vehicle, so that the task completion time and energy consumption of the unmanned aerial vehicle are further reduced, and meanwhile, task balance is realized. Can be widely popularized in the fields of wireless communication and the like.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. The unmanned aerial vehicle data acquisition task distribution method for the space-time constraint network is characterized by comprising the following steps of:
s1: establishing a space-time constraint data acquisition network, wherein the space-time constraint data acquisition network comprises an unmanned aerial vehicle set U= { U formed by m unmanned aerial vehicles 1 ,u 2 ,…,u i ,…,u m Task set s= { S composed of n task requesters } 1 ,s 2 ,…,s j ,…,s n -and a spatial crowdsourcing server;
s2: acquiring position coordinates of an ith unmanned aerial vehicle, real-time energy of the ith unmanned aerial vehicle, position coordinates of a jth task requester, transmitting power of the jth task requester and remaining effective time of the jth task, namely expiration time;
s3: according to the position coordinates of the ith unmanned aerial vehicle and the position coordinates of the jth task requester, acquiring the distance d between the ith unmanned aerial vehicle and the jth task requester ij The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the position of the jth task requester is in the working area of the ith unmanned aerial vehicle;
s4: if the position of the jth task requester is in the working area of the ith unmanned aerial vehicle, acquiring the current state of the ith unmanned aerial vehicle and the task state of the jth task requester;
s5: when the ith unmanned aerial vehicle is in an idle state currently and the task of the jth task requester is in an unexecuted state, executing S6;
s6: according to the transmitting power of the jth task requester, acquiring the signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the task of the jth task requester and the energy consumption of the ith unmanned aerial vehicle when executing the task of the jth task requester;
the signal-to-interference-and-noise ratio when the ith unmanned aerial vehicle and the jth task requester are subjected to task acquisition is as follows:
wherein SINR ij The signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the jth task is set; p (P) j The transmit power for the j-th task requester; lambda is the wavelength; g is the antenna direction coefficient of the task requester; i is interference when other unmanned aerial vehicles collect data; sigma (sigma) 2 The noise power of the unmanned aerial vehicle;is the path loss; />Signal representing single reference distance d=1mChannel power gain; d, d ij Representing the distance from the jth task requester to the ith unmanned plane position;
the energy consumption of the ith unmanned aerial vehicle for executing the task of the jth task requester is obtained as follows:
wherein E is ij Executing the energy consumption of the task of the jth task requester for the ith unmanned aerial vehicle;is flight energy consumption; />Is hovering energy consumption; />The energy consumption for information transmission; p (P) i fly The flight power of the ith unmanned aerial vehicle; p (P) i hover The power of the spiral of the ith unmanned aerial vehicle is calculated; p (P) i commu The communication power of the ith unmanned aerial vehicle; />The flight time of the ith unmanned aerial vehicle; />The hover time for the ith unmanned aerial vehicle; />The communication time of the ith unmanned aerial vehicle;
s7: determining whether the ith unmanned aerial vehicle can execute the task of the jth task requester, and acquiring a task set which can be executed by the ith unmanned aerial vehicle; distributing unmanned aerial vehicle data acquisition tasks according to the residual effective time of the tasks to obtain task distribution signals;
the task method for determining whether the ith unmanned aerial vehicle can execute the jth task requester is as follows:
if SINR ij ≥SINR th And is also provided withThe ith unmanned aerial vehicle can execute the task of the jth task requester;
wherein SINR ij The signal-to-interference-and-noise ratio of the ith unmanned aerial vehicle when executing the jth task is set; SINR (Signal to interference plus noise ratio) th Is a signal-to-interference-and-noise ratio threshold; e (E) ij The energy consumption for executing the j-th task for the i-th unmanned aerial vehicle; e (E) th Is an unmanned energy consumption threshold;
wherein E is i Real-time energy of the ith unmanned aerial vehicle;the return energy consumption of the ith unmanned aerial vehicle; />The return energy consumption of the j-th task is executed for the i-th unmanned aerial vehicle; />Is the return time of the ith unmanned aerial vehicle, v i The flight speed of the ith unmanned aerial vehicle; />The return distance of the ith unmanned aerial vehicle; />The coordinate of the x axis of the initial position of the ith (i is more than or equal to 1 and less than or equal to m) frame unmanned aerial vehicle; />The y-axis coordinate of the initial position of the ith unmanned aerial vehicle;
the method for distributing the unmanned aerial vehicle data acquisition tasks comprises the following steps:
s71: according to whether the ith unmanned aerial vehicle can execute the task of the jth task requester, acquiring a task vector which can be executed by the ith unmanned aerial vehicle
Wherein S is i A task vector representing the i-th unmanned aerial vehicle being able to execute;representing the ith task that the ith unmanned aerial vehicle is capable of performing; p represents the number of tasks that the ith unmanned can perform; p represents the total number of tasks that the ith unmanned can perform;
s72: when p=1, the number of the active groups,the i-th unmanned can only execute task at the moment +.>Thus task->Is executed by the ith unmanned aerial vehicle, namely the final execution task of the ith unmanned aerial vehicle is +.>
S73: when P>1, the time is 1; according to the task set vector which can be executed by the ith unmanned aerial vehicle, a task vector set { S ] which can be executed by all unmanned aerial vehicles is obtained 1 ,S 2 ,...,S i ,...,S m -a }; to obtain and be able to performUnmanned aerial vehicle vector of (a) Wherein (1)>Can perform +.>Is an unmanned aerial vehicle; q is capable of executing task->Is a total number of unmanned aerial vehicles;
if the task is executedWhen there is no Q>1-> Any of the tasksThe method comprises the steps that the method can only be executed by an ith unmanned aerial vehicle, and then a task finally executed by the ith unmanned aerial vehicle is obtained through a quality evaluation algorithm;
if Q is present>1, then There is a task->Can be executed by a Q frame unmanned aerial vehicle
Then pair Each task of->Executing reverse auction algorithm to determine task->Is preferably performed unmanned aerial vehicle->
If there is no p-th task that the i-th unmanned aerial vehicle can executeIs preferably performed unmanned aerial vehicle->The z-th task which can be performed with the i-th unmanned aerial vehicle +.>Is preferably performed unmanned aerial vehicle->When the same, then the ith task that the ith unmanned aerial vehicle can perform +.>For said preferred execution unmanned aerial vehicle +.>Is executed in the first place; wherein,
if there is the ith task that the ith unmanned aerial vehicle can executeIs preferably performed unmanned aerial vehicle->The z-th task which can be performed with the i-th unmanned aerial vehicle +.>Is preferably performed unmanned aerial vehicle->In the same case, i.e.)>I.e. < ->At the same time->And->Is to obtain +.>Is executed in the first place;
specifically, obtain respectivelyExecution->And->The energy required during this process is selected as the task with the small energy required>Is executed in the first place;
s74: if presentNot belong to->When the preferred execution of any one of the tasks is unmanned aerial vehicle,
if already presentAs a final execution task for the ith unmanned aerial vehicle or preferably for the execution of unmanned aerial vehicle +.>According to +.>Acquisition of ∈10 by quality assessment algorithm>Is executed in the first place;
otherwise according to S= { S 1 ,s 2 ,…,s j ,…,s n Acquisition by quality assessment algorithmIs executed in the first place;
s8: and the space crowdsourcing server sends a task distribution signal to the unmanned aerial vehicle, so that the unmanned aerial vehicle completes the data acquisition task of the task requester.
2. The unmanned aerial vehicle data acquisition task allocation method for space-time constraint network according to claim 1, wherein in S3, the method for judging whether the position of the jth task requester is in the working area of the ith unmanned aerial vehicle is as follows:
if it is
The position of the jth task requester is located in the working area of the ith unmanned aerial vehicle;
wherein,
wherein x is i An x-axis coordinate of a real-time position for the ith (i=1, 2, …, m) frame of the drone; y is i The y-axis coordinate of the real-time position of the ith unmanned aerial vehicle; d, d ij Distance from the jth task requester to the ith unmanned plane position; x is x j X-axis coordinates for the j (j=1, 2, …, n) th task requester; y is j Y-axis coordinates for the jth task requester; i is the number of the unmanned aerial vehicle, and m is the total amount of the unmanned aerial vehicle; j is the number of the task requesters, n is the total number of the task requesters; x is x i The x-axis coordinate of the position of the ith unmanned aerial vehicle; y is i The y-axis coordinate of the position of the ith unmanned aerial vehicle;radius of coverage area for the ith unmanned aerial vehicle.
3. The unmanned aerial vehicle data acquisition task allocation method for the space-time constraint network according to claim 1, wherein the task method for finally executing the ith unmanned aerial vehicle is obtained through a quality evaluation algorithm:
if the ith unmanned aerial vehicle executes the p-th taskIn this case, the energy required is +.>The i-th unmanned aerial vehicle finally executes the task as a task +.>
Wherein E is ip The energy consumption of the p-th task which can be executed by the i-th unmanned aerial vehicle.
4. The unmanned aerial vehicle data acquisition task allocation method for space-time constraint network according to claim 1, wherein the p-th task which can be executed by the i-th unmanned aerial vehicle is determined according to a reverse auction algorithmIs preferably performed unmanned aerial vehicle->The method comprises the following steps:
acquisition ofTask waiting time of the q-th unmanned aerial vehicle in (2)>
When (when)In the time-course of which the first and second contact surfaces,
if it meets the requirement, whenUnmanned aerial vehicle->The time for executing the task is
The ith task that the ith unmanned aerial vehicle is able to performIs preferably performed unmanned aerial vehicle->
Wherein,to be able to perform the ith task that the ith unmanned aerial vehicle is able to perform/>Task waiting time of the q-th unmanned aerial vehicle in the unmanned aerial vehicle set; />Is a latency threshold;
wherein,t p remaining active time for the p-th task; />The task transmission time for the p-th task.
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