A method of aircraft collaborative task allocation combined with virtual technology

Abstract

The present invention relates to a method for allocating collaborative tasks of aircraft combined with virtual technology, and belongs to the field of task allocation technology. The method comprises the following steps: S1: taking a method for allocating collaborative tasks of unmanned aerial vehicle formations combined with virtual technology for data collection as an example, obtaining relevant information of Internet of Things devices in the area where unmanned aerial vehicles perform data collection through field investigation, obtaining basic information of the unmanned aerial vehicle used through unmanned aerial vehicle model reference diagram, and modeling it, and further building a virtual scene through the Unity3D virtual simulation platform. The present invention proposes a method for allocating collaborative tasks of aircraft combined with virtual technology, taking a method for allocating collaborative tasks of unmanned aerial vehicle formations combined with virtual technology for data collection as an example, by building a virtual scene for unmanned aerial vehicle data collection, it is convenient to simulate the actual data collection process.

Description

Technical Field

[0001] The invention relates to an aircraft collaborative task allocation method combined with virtual technology, belonging to the technical field of task allocation. Background Art

[0002] In recent years, drone technology has matured and its performance has been improved. The scope of tasks that can be undertaken has been further expanded, and the task level has been continuously improved. The invention patent with the publication number CN115454147B discloses "a method for cooperative task allocation of drone clusters based on dynamic optimization, which includes: triggering the start condition of cooperative task allocation of drone clusters, sending task instructions to drone clusters; planning future tasks of drone clusters, obtaining future task information, and building a task path transfer model; using the drone cluster cooperative task allocation optimization model to obtain preliminary results of task allocation of drone clusters; using the task path transfer model to process the preliminary results of task allocation, obtaining the final task allocation results of drone clusters, and completing the cooperative task allocation of drone clusters. The present invention efficiently and reasonably allocates various tasks to drone formations, so that various performance indicators of the system can reach extreme values ​​as much as possible, giving full play to the cooperative work efficiency of drone formations, and greatly improving the effectiveness and real-time performance of drone task allocation."

[0003] This prior art only solves the existing UAV cluster task allocation method, and regards the assigned tasks as the same category. It does not classify the tasks according to the characteristics of the tasks performed by the UAV at each stage. At the same time, the collaborative task allocation method it adopts is inefficient and has poor reliability. It does not take into account the need to consider the freshness of the collected data when collecting data through UAVs, and when there is overlap in the signal range of sensor nodes, it is necessary to adjust the hovering position of the UAV at the corresponding sensor node to ensure the normal data transmission, and when planning the flight trajectory of the UAV, it is necessary to consider the possibility of UAV flight collision. Summary of the invention

[0004] The purpose of the present invention is to provide a method for collaborative task allocation of aircraft combined with virtual technology. Taking the method for collaborative task allocation of unmanned aerial vehicle formation combined with virtual technology for data collection as an example, by building a virtual scene for unmanned aerial vehicle data collection, it is convenient to simulate the actual data collection process.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for allocating aircraft collaborative tasks combined with virtual technology, which is characterized in that it includes the following steps:

[0006] S1: Taking the UAV formation collaborative task allocation method combined with virtual technology for data collection as an example, the relevant information of the IoT devices in the area where the UAV performs data collection is obtained through field investigation, the basic information of the UAV used is obtained through the UAV model reference map, and a model is built for it, and a virtual scene is further built through the Unity3D virtual simulation platform;

[0007] S2: Modeling is done with the goal of improving the freshness of the information collected by the drone, establishing the association between the drone and the data collection point, and between the data collection point and the sensor node, and further with the optimization goal of improving the efficiency of the drone in performing data collection, designing the way the drone collects data at the sensor node;

[0008] S3: Based on the data collection method of the drone at different sensor nodes designed in step S2 and the new hovering position of the drone at different sensor nodes, the flight trajectory of the drone is planned, and when a drone collision occurs in the virtual scene, the flight trajectory of the drone is replanned.

[0009] Preferably, step S1 also includes taking the drone formation collaborative task allocation method combined with virtual technology for data collection as an example, obtaining relevant information of the Internet of Things devices in the data collection area executed by the drone through field investigation, including sensor node information, data collection point information in the sensor node and data collection center information, and obtaining the basic information of the drone used through the drone model reference map, building a virtual simulation scene of the collection area through the Unity3D virtual simulation platform, building a virtual scene model of the Internet of Things device through the 3DMax modeling software, and using LOD technology to optimize the model.

[0010] Preferably, step S1 also includes importing the model reference image of the drone into 3DMax modeling software, modeling the drone, and optimizing the model using LOD technology, adding the drone model file in obj format exported from the 3DMax modeling software and the virtual scene model file of the Internet of Things device to the Project panel of the Unity3D virtual simulation platform, and then adding the drone model and the virtual scene model of the Internet of Things device to the constructed virtual simulation scene, and giving the drone model a unique name.

[0011] Preferably, step S2 further includes setting a sensor node to belong to only one data collection point, and a data collection point can collect data from multiple sensor nodes within its range, and setting the hovering position of the drone at each data collection point to be a fixed position, and each hovering position is at the same height. When the drone reaches the hovering position of the data collection point, the sensor nodes in the data collection point are collected using a time division multiple access method, that is, the data packet upload time of the sensor node is only related to the length of the data packet. The data upload time of the sensor is calculated by a data transmission rate algorithm, and the specific calculation formula is as follows:

[0012] ;

[0013] In the formula, Indicates data collection point Internal sensor node Data upload time, represents the expected path loss of the data transmission channel, Represents a sensor node The length of the generated packet, Represents a sensor node The data upload rate of the drone is , and the drone needs to return to the data collection center to unload the data after the data collection is completed, and upload the collected data to the data collection center. The data unloading rate algorithm is used to calculate the data unloading rate of the drone in the data collection center. The specific calculation formula is as follows:

[0014] ;

[0015] In the formula, represents the data offloading rate of the UAV at the data collection center, represents the noise power in the system bandwidth, represents the noise power of the drone, Indicates the transmitting power of the drone, Indicates that the distance is equal to the height of the drone The path loss on the line-of-sight link is Indicates the channel power gain at a reference distance of 1 meter.

[0016] Preferably, step S2 also includes that the data information freshness of the sensor node is specifically defined as the time interval from the drone starting to collect the sensor node data to completing the unloading at the data center, specifically including the flight time of the drone, the data upload time of the sensor node and the unloading time when the drone arrives at the data center to complete the data unloading, and the flight time of the drone is set not to exceed the flight time of the drone, that is, the data information freshness of the first sensor node collected by the drone when performing the data collection task is the largest among all the sensor nodes included in the trajectory of this task, and the average data information freshness is calculated, and the maximum data information freshness and the average data information freshness are minimized. The specific operation is that for the data collection points within the drone signal range, density clustering and setting a clustering threshold limit are used to make the range of the data collection points non-overlapping, thereby avoiding collisions between drones during data collection, and based on the latitude and longitude positions of each sensor node and the data upload time when the drone collects data at the sensor node, the time distance and spatial distance between the two sensor nodes are calculated. Calculation, the sensor node set in the spatiotemporal neighborhood of any sensor node can be obtained by aggregating the sensor nodes existing within the radius of the drone signal range with the sensor node position as the center, and establishing the association between the sensor node and the data collection point based on the data collection point information corresponding to each sensor node in the sensor node set, and further planning the path of the drone through the path planning algorithm based on the information of each data collection point included in the data collection task of the drone through the CUKK-means algorithm, and calculating the time for the drone to complete the data collection of all sensor nodes based on the path planning result, and when the calculation result is greater than the endurance of the drone, the algorithm is iterated, otherwise the planned path is executed, and when the algorithm is iterated, the data collection points are divided into two parts, the maximum value of the time required for the drone to complete the data collection task of the sensor nodes corresponding to the two parts is added to the time for the drone to return to the data collection center to complete the data unloading, and compared with the endurance of the drone, when the former is less than the latter, the optimal route planning operation is performed, otherwise the algorithm iteration continues, and the number of drones is increased by one.

[0017] Preferably, step S2 also includes designing a method for the drone to collect data at a data collection point with the goal of improving the efficiency of the drone in performing data collection as an optimization goal, specifically, based on the signal transmission radius of the sensor node and the spatial distance between two adjacent sensor nodes, comparing twice the signal transmission radius of the sensor node with the spatial distance between two adjacent sensor nodes. If the spatial distance between two adjacent sensor nodes is not less than twice the signal transmission radius of the sensor node, it indicates that the signal transmission ranges of the two adjacent sensor nodes do not overlap, otherwise it indicates that the signal transmission ranges of the two adjacent sensor nodes overlap, and the sensor nodes with overlapping signal transmission ranges are marked, and the overlapping area is marked, and the drone is further set to collect data at a hovering point at a sensor node with non-overlapping signal transmission ranges, and the drone collects data within the signal transmission range of the sensor node with overlapping signal transmission ranges, away from the overlapping area and closest to the corresponding hovering point originally set, and the position is set as the new hovering point of the sensor node, thereby facilitating subsequent route planning while ensuring the quality of data transmission.

[0018] Preferably, step S3 also includes performing an optimal route planning operation on the flight trajectory of the drone when performing the data collection task based on the data collection mode of the drone at different sensor nodes designed in step S2, and setting the new hovering position of the drone at different sensor nodes, specifically solving the optimal route for the flight trajectory of each drone when performing the data collection task through an ant colony algorithm, and in the algorithm iteration process, the drone starts from the data collection center and selects the next data collection point to be reached according to the heuristic information and the transition probability until it passes each data collection point associated with the drone, and finally the drone returns to the data center and generates a path in the iteration, and when the number of iterations reaches the preset number of iterations, the maximum data information freshness and the average data information freshness of each iteration path are calculated, and the weights of the maximum data information freshness and the average data information freshness are set to 0.5, and then the final data information freshness is calculated, and the iteration path with the smallest calculation result is selected as the optimal flight trajectory.

[0019] Preferably, step S3 also includes simulating the optimal flight trajectory of each drone in a virtual scene. When a drone collision occurs, based on the final data information freshness value of the drone, the lower the final data information freshness value of the drone is set, the higher the priority of the corresponding drone is, and then the flight trajectory of the drone with a lower priority among the two colliding drones is replanned while avoiding the flight trajectory of the drone with a higher priority.

[0020] Compared with the prior art, the beneficial effects of the present invention at least include: the present invention proposes a method for aircraft collaborative task allocation combined with virtual technology, taking the method for drone formation collaborative task allocation combined with virtual technology for data collection as an example, by building a virtual scene for drone data collection, it is convenient to simulate the actual data collection process, and when collecting data from the drone, the information freshness of the drone collected data is considered, and it is used as a selection indicator for the optimal route planning of the drone, and when the signal transmission range of the sensor node overlaps with that of the adjacent sensor node, the drone hovering position originally preset by the corresponding sensor node is changed, and it is used as the trajectory point for the subsequent drone route planning, and the optimal flight trajectory of each drone is simulated in the virtual scene, and when a drone collision occurs, the flight trajectory of the low-priority drone is replanned based on the priority of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flowchart of a method for allocating collaborative tasks of aircraft combined with virtual technology. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 The present invention provides a technical solution: a method for allocating aircraft collaborative tasks combined with virtual technology, comprising the following steps:

[0024] S1: Taking the UAV formation collaborative task allocation method combined with virtual technology for data collection as an example, the relevant information of the IoT devices in the area where the UAV performs data collection is obtained through field investigation, the basic information of the UAV used is obtained through the UAV model reference map, and a model is built for it, and a virtual scene is further built through the Unity3D virtual simulation platform;

[0025] S2: Modeling is done with the goal of improving the freshness of the information collected by the drone, establishing the association between the drone and the data collection point, and between the data collection point and the sensor node, and further with the optimization goal of improving the efficiency of the drone in performing data collection, designing the way the drone collects data at the sensor node;

[0026] S3: Based on the data collection method of the drone at different sensor nodes designed in step S2 and the new hovering position of the drone at different sensor nodes, the flight trajectory of the drone is planned, and when a drone collision occurs in the virtual scene, the flight trajectory of the drone is replanned.

[0027] Step S1 also includes taking the UAV formation collaborative task allocation method combined with virtual technology for data collection as an example, obtaining relevant information of IoT devices in the area where the UAV performs data collection through field investigation, including sensor node information, data collection point information in the sensor node, and data collection center information, and obtaining basic information of the used UAV through a UAV model reference map, building a virtual simulation scene of the collection area through the Unity3D virtual simulation platform, building a virtual scene model of the IoT device through the 3DMax modeling software, and using LOD technology to optimize the model;

[0028] Step S1 also includes importing the model reference image of the drone into the 3DMax modeling software, modeling the drone, and optimizing the model using the LOD technology, adding the drone model file in the obj format exported from the 3DMax modeling software and the virtual scene model file of the IoT device to the Project panel of the Unity3D virtual simulation platform, and then adding the drone model and the virtual scene model of the IoT device to the constructed virtual simulation scene, and naming the drone model without duplication;

[0029] Step S2 also includes setting a sensor node to belong to only one data collection point, and a data collection point can collect data from multiple sensor nodes within its range, and setting the hovering position of the drone at each data collection point to be a fixed position, and each hovering position is at the same height. When the drone reaches the hovering position of the data collection point, the sensor nodes in the data collection point are collected using a time division multiple access method, that is, the data packet upload time of the sensor node is only related to the length of the data packet. The data upload time of the sensor is calculated by the data transmission rate algorithm, and the specific calculation formula is as follows:

[0030] ;

[0031] In the formula, Indicates data collection point Internal sensor node Data upload time, represents the expected path loss of the data transmission channel, Represents a sensor node The length of the generated packet, Represents a sensor node The data upload rate of the drone is , and the drone needs to return to the data collection center to unload the data after the data collection is completed, and upload the collected data to the data collection center. The data unloading rate algorithm is used to calculate the data unloading rate of the drone in the data collection center. The specific calculation formula is as follows:

[0032] ;

[0033] In the formula, represents the data offloading rate of the UAV at the data collection center, represents the noise power in the system bandwidth, represents the noise power of the drone, Indicates the transmitting power of the drone, Indicates that the distance is equal to the height of the drone The path loss on the line-of-sight link is Indicates the channel power gain at a reference distance of 1 meter;

[0034] Step S2 also includes that the data information freshness of the sensor node is specifically defined as the time interval from the drone starting to collect the sensor node data to completing the unloading at the data center, specifically including the flight time of the drone, the data upload time of the sensor node and the unloading time when the drone arrives at the data center to complete the data unloading, and the flight time of the drone is set not to exceed the endurance of the drone, that is, the data information freshness of the first sensor node collected by the drone when performing the data collection task is the largest among all the sensor nodes included in the trajectory of this task, and the average data information freshness is calculated, and the maximum data information freshness and the average data information freshness are minimized. The specific operation is that for the data collection points within the drone signal range, density clustering and setting a clustering threshold limit are used to make the range of the data collection points non-overlapping, thereby avoiding collisions between drones during data collection, and based on the latitude and longitude positions of each sensor node and the data upload time when the drone collects data at the sensor node, the time distance and spatial distance between the two sensor nodes are calculated. The sensor node set in the spatiotemporal neighborhood of any sensor node can be obtained by aggregating the sensor nodes existing within the radius of the drone signal range with the sensor node position as the center, and establishing the association between the sensor node and the data collection point based on the data collection point information corresponding to each sensor node in the sensor node set. Further, the CUKK-means algorithm is used to plan the path of the drone based on the information of each data collection point included in the data collection task of the drone through the path planning algorithm, and the time for the drone to complete the data collection of all sensor nodes is calculated based on the path planning result. When the calculated result is greater than the endurance of the drone, the algorithm is iterated, otherwise the planned path is executed. When iterating the algorithm, the data collection points are divided into two parts, and the maximum value of the time required for the drone to complete the data collection task of the sensor nodes corresponding to the two parts is added to the time for the drone to return to the data collection center to complete the data unloading, and compared with the endurance of the drone. When the former is less than the latter, the optimal route planning operation is performed, otherwise the algorithm iteration continues, and the number of drones is increased by one.

[0035] Step S2 also includes designing a method for the drone to collect data at a data collection point with the goal of improving the efficiency of the drone in performing data collection as an optimization goal, specifically, based on the signal transmission radius of the sensor node and the spatial distance between two adjacent sensor nodes, comparing twice the signal transmission radius of the sensor node with the spatial distance between two adjacent sensor nodes, if the spatial distance between two adjacent sensor nodes is not less than twice the signal transmission radius of the sensor node, it indicates that the signal transmission ranges of the two adjacent sensor nodes do not overlap, otherwise it indicates that the signal transmission ranges of the two adjacent sensor nodes overlap, and marking the sensor nodes with overlapping signal transmission ranges, marking the overlapping area, and further setting the drone to collect data at a hovering point at the sensor node with non-overlapping signal transmission ranges, the drone collects data within the signal transmission range of the sensor node with overlapping signal transmission ranges, away from the overlapping area and closest to the corresponding hovering point originally set, and setting the position as a new hovering point of the sensor node, thereby facilitating subsequent route planning while ensuring the quality of data transmission;

[0036] Step S3 also includes performing an optimal route planning operation on the flight trajectory of the drone when performing the data collection task based on the data collection method of the drone at different sensor nodes designed in step S2 and the new hovering position of the drone at different sensor nodes. Specifically, the optimal route of the flight trajectory of each drone when performing the data collection task is solved by using an ant colony algorithm, and in the algorithm iteration process, the drone starts from the data collection center and selects the next data collection point to be reached according to the heuristic information and the transition probability until it passes through each data collection point associated with the drone. Finally, the drone returns to the data center and generates a path in the iteration. When the number of iterations reaches the preset number of iterations, the maximum data information freshness and the average data information freshness of each iteration path are calculated, and the weights of the maximum data information freshness and the average data information freshness are set to 0.5, and then the final data information freshness is calculated, and the iteration path with the smallest calculation result is selected as the optimal flight trajectory;

[0037] Step S3 also includes simulating the optimal flight trajectory of each drone in a virtual scene. When a drone collision occurs, based on the final data information freshness value of the drone, the lower the final data information freshness value of the drone is set, the higher the priority of the corresponding drone is, and then the flight trajectory of the drone with a lower priority among the two colliding drones is replanned while avoiding the flight trajectory of the drone with a higher priority.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for allocating aircraft collaborative tasks combined with virtual technology, characterized in that The following steps are involved: S1: Taking the UAV formation collaborative task allocation method combined with virtual technology for data collection as an example, the relevant information of the IoT devices in the area where the UAV performs data collection is obtained through field investigation, the basic information of the UAV used is obtained through the UAV model reference map, and a model is built for it, and a virtual scene is further built through the Unity3D virtual simulation platform; S2: Modeling is done with the goal of improving the freshness of the information collected by the drone, establishing the association between the drone and the data collection point, and between the data collection point and the sensor node, and further with the optimization goal of improving the efficiency of the drone in performing data collection, designing the way the drone collects data at the sensor node; S3: Based on the data collection method of the drone at different sensor nodes designed in step S2 and the new hovering positions of the drone at different sensor nodes, the flight trajectory of the drone is planned, and when a drone collision occurs in the virtual scene, the flight trajectory of the drone is replanned; The step S1 also includes taking the drone formation collaborative task allocation method combined with virtual technology for data collection as an example, obtaining relevant information of the Internet of Things devices in the area where the drone performs data collection through field investigation, including sensor node information, data collection point information in the sensor node, and data collection center information, and obtaining basic information of the drone used through the drone model reference map, building a virtual simulation scene of the collection area through the Unity3D virtual simulation platform, building a virtual scene model of the Internet of Things device through the 3DMax modeling software, and using LOD technology to optimize the model; The step S2 also includes that the data information freshness of the sensor node is specifically defined as the time interval from the drone starting to collect the sensor node data to completing the unloading at the data center, specifically including the flight time of the drone, the data upload time of the sensor node and the unloading time when the drone arrives at the data center to complete the data unloading, and the flight time of the drone is set not to exceed the endurance of the drone, that is, the data information freshness of the first sensor node collected by the drone when performing the data collection task is the largest among all the sensor nodes included in the task trajectory, and the average data information freshness is calculated, and the maximum data information freshness and the average data information freshness are minimized; The step S3 also includes performing an optimal route planning operation on the flight trajectory of the drone when performing the data collection task based on the data collection method of the drone at different sensor nodes designed in step S2, and setting the drone at a new hovering position at different sensor nodes, specifically solving the optimal route for the flight trajectory of each drone when performing the data collection task by using an ant colony algorithm, and in the algorithm iteration process, the drone starts from the data collection center and selects the next data collection point to be reached according to the heuristic information and the transition probability until it passes through each data collection point associated with the drone, and finally the drone returns to the data center and generates a path in the iteration, and when the number of iterations reaches the preset number of iterations, the maximum data information freshness and the average data information freshness of each iteration path are calculated, and the weights of the maximum data information freshness and the average data information freshness are set to 0.5, and then the final data information freshness is calculated, and the iteration path with the smallest calculation result is selected as the optimal flight trajectory; The step S3 also includes simulating the optimal flight trajectory of each drone in the virtual scene. When a drone collision occurs, based on the final data information freshness value of the drone, the lower the final data information freshness value of the drone is set, the higher the priority of the corresponding drone is, and then the flight trajectory of the drone with a lower priority among the two colliding drones is replanned while avoiding the flight trajectory of the drone with a higher priority.

2. The method for allocating aircraft collaborative tasks combined with virtual technology according to claim 1, characterized in that: The step S1 also includes importing the model reference image of the drone into the 3DMax modeling software, modeling the drone, and optimizing the model using LOD technology, adding the drone model file in obj format exported from the 3DMax modeling software and the virtual scene model file of the IoT device to the Project panel of the Unity3D virtual simulation platform, and then adding the drone model and the virtual scene model of the IoT device to the constructed virtual simulation scene, and giving the drone model a unique name.

3. The method for allocating aircraft collaborative tasks combined with virtual technology according to claim 1, characterized in that: The step S2 also includes setting a sensor node to belong to only one data collection point, and a data collection point can collect data from multiple sensor nodes within its range, and setting the hovering position of the drone at each data collection point to be a fixed position, and each hovering position is at the same height. When the drone reaches the hovering position of the data collection point, the sensor nodes in the data collection point are collected using time division multiple access, that is, the data packet upload time of the sensor node is only related to the length of the data packet. The data upload time of the sensor is calculated by a data transmission rate algorithm, and after the data collection is completed, the drone needs to return to the data collection center for data unloading, and upload the collected data to the data collection center. The data unloading rate of the drone at the data collection center is calculated by a data unloading rate algorithm.

4. The method for allocating aircraft collaborative tasks combined with virtual technology according to claim 1, characterized in that: The step S2 also includes designing a method for the drone to collect data at a data collection point with the optimization goal of improving the efficiency of the drone in performing data collection. Specifically, based on the signal transmission radius of the sensor node and the spatial distance between two adjacent sensor nodes, twice the signal transmission radius of the sensor node is compared with the spatial distance between two adjacent sensor nodes. If the spatial distance between two adjacent sensor nodes is not less than twice the signal transmission radius of the sensor nodes, it indicates that the signal transmission ranges of the two adjacent sensor nodes do not overlap. Otherwise, it indicates that the signal transmission ranges of the two adjacent sensor nodes overlap. The sensor nodes with overlapping signal transmission ranges are marked, and the overlapping area is marked.

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