CN-122002598-A - Unmanned aerial vehicle general sense computing resource allocation method and system for path task coverage

CN122002598ACN 122002598 ACN122002598 ACN 122002598ACN-122002598-A

Abstract

The invention relates to an unmanned aerial vehicle general sense computing resource allocation method and system for covering path tasks, and belongs to the technical field of wireless communication. The method comprises the steps of constructing a topology model according to geographic information of a target area, deploying a ground user terminal, respectively constructing an unmanned aerial vehicle perception quality model, a communication rate model between the unmanned aerial vehicle and the ground terminal, a calculation model of unmanned aerial vehicle data generation rate and an energy consumption model of total instantaneous power of the unmanned aerial vehicle, determining a decision variable to be optimized according to the energy consumption model, setting a plurality of constraint conditions based on the perception quality model, the communication rate model and the calculation model, obtaining an instantaneous minimum power function according to the constraint conditions and the energy consumption model, obtaining optimal speed of the unmanned aerial vehicle on an optimal path according to the function, generating optimal perception power, communication power and calculation frequency according to the optimal speed, and completing allocation of general sensing calculation resources of the unmanned aerial vehicle. The invention improves the adaptability and the robustness of the unmanned aerial vehicle general sense computing resource allocation.

Inventors

YIN YILIN
HOU PENG
WANG JIN
TAN QICHEN

Assignees

苏州大学

Dates

Publication Date: 20260508
Application Date: 20260408

Claims (10)

1. An unmanned aerial vehicle general sense computing resource allocation method for covering path tasks is characterized by comprising the following steps: S1, constructing a topology model of a target area according to geographic information of the target area, and deploying a ground user terminal in a geographic range covered by the topology model; S2, constructing a perception quality model of the unmanned aerial vehicle, a communication rate model between the unmanned aerial vehicle and the ground user terminal, a calculation model of the data generation rate of the unmanned aerial vehicle and an energy consumption model of the total instantaneous power of the unmanned aerial vehicle; S3, constructing a weighted cost function according to the energy consumption model, taking the weighted cost function as an optimization target, determining a decision variable to be optimized according to the optimization target, and determining a plurality of constraint conditions according to the decision variable to be optimized, the perceived quality model, the communication rate model and the calculation model; S4, obtaining nodes with odd degrees according to the topology model, constructing a first set, constructing a mixed weight complete graph according to the first set, calculating the weight of the mixed weight complete graph edge, and carrying out amplification on an undirected graph of the topology model according to the mixed weight complete graph and the weight to obtain an augmented undirected graph; S5, obtaining an optimal communication power closed solution, an optimal perception power closed solution and an optimal calculation frequency closed solution according to the constraint conditions, obtaining an instantaneous minimum power function according to the optimal communication power closed solution, the optimal perception power closed solution, the optimal calculation frequency closed solution and the energy consumption model, calculating an optimal multiplier according to the instantaneous minimum power function and the total length of the optimal path, calculating the optimal speed of the unmanned aerial vehicle according to the optimal multiplier, and generating the optimal perception power, the optimal communication power and the optimal calculation frequency according to the optimal speed to finish the communication calculation resource allocation of the unmanned aerial vehicle.
2. The unmanned aerial vehicle general sensing computing resource allocation method for covering path tasks according to claim 1, wherein in the step S5, an optimal multiplier is calculated according to the instantaneous minimum power function and the optimal path total length, and the step of calculating the optimal speed of the unmanned aerial vehicle according to the optimal multiplier is as follows: Constructing a local Hamiltonian according to the instantaneous minimum power function; constructing a dual expression according to the local Hamiltonian amount, the total length of the optimal path and the total capacity of the unmanned aerial vehicle-mounted battery; Performing iterative search on the dual expression by adopting a dichotomy, and solving the instantaneous flying speed for each path position in each iterative process until the global energy constraint is met, so as to obtain an optimal multiplier; And calculating the optimal speed of the unmanned aerial vehicle according to the optimal multiplier.
3. The unmanned aerial vehicle general sensing computing resource allocation method for covering path tasks according to claim 1, wherein in S5, the expressions of the optimal communication power closed-form solution, the optimal perceived power closed-form solution, and the optimal computation frequency closed-form solution are respectively: ; ; ; Wherein, the The closed-form solution of the optimal communication power is represented, Representing the channel gain of the bottleneck user within the unmanned dynamic coverage radius, Representing the minimum communication rate threshold value, Indicating the position of the unmanned plane in the path A set of users within a coverage area, Representing the threshold value constant and, Representing the energy efficiency constant of the unmanned aerial vehicle remote sensing camera system, The bandwidth is represented by a bandwidth that, Which represents the power of the noise and, Representing the number of CPU cycles required to calculate the unit data, Indicating the instantaneous speed of flight of the vehicle, Representing the lowest perceived quality threshold value, Indicating the rate of generation of the remote sensing data, The pixel size is indicated as such, The primary distance is indicated as such, Indicating the relative altitude of the flight, Representing the width of the fixed ground strap covered by the camera sensor, Representing the color bit depth of the image.
4. The unmanned aerial vehicle general sensing computing resource allocation method for coverage path tasks according to claim 1, wherein in S2, the expression of the perceived quality model is: ; Wherein, the Representing the instantaneous perceived quality of the image, Representing the energy efficiency constant of the unmanned aerial vehicle remote sensing camera system, Indicating the relative altitude of the flight, The primary distance is indicated as such, The pixel size is indicated as such, Representing the instantaneous perceived power of the device, Indicating the instantaneous speed of flight of the vehicle, Indicating the path position index.
5. The unmanned aerial vehicle general sensing computing resource allocation method for the coverage path task according to claim 1, wherein in S2, the expression of the communication rate model between the unmanned aerial vehicle and the ground user terminal is: ; Wherein, the Representing the transmission efficiency factor of the transmission, Indicating the position of the unmanned plane in the path Location and the first The instantaneous communication rate of the individual ground user terminals, The bandwidth is represented by a bandwidth that, Representing the power of the gaussian white noise, The doppler coefficient is represented as such, Indicating the instantaneous speed of flight of the vehicle, Which is indicative of the transmit power, Representing the large scale channel gain.
6. The unmanned aerial vehicle general sensing computing resource allocation method for covering path tasks according to claim 1, wherein in S2, the expression of the calculation model of unmanned aerial vehicle data generation rate is: ; Wherein, the Representing the data generation rate of the unmanned aerial vehicle, Representing the width of the fixed ground strap covered by the camera sensor, Representing the color bit depth of the image, Indicating the instantaneous speed of flight of the vehicle, Representing the instantaneous perceived quality.
7. The unmanned aerial vehicle general sensing computing resource allocation method for covering path tasks according to claim 1, wherein in S2, the expression of the energy consumption model of the unmanned aerial vehicle total instantaneous power is: ; Wherein, the Indicating the position of the unmanned plane in the path The total instantaneous power at which the power is calculated, Which represents the propulsive power and, Indicating the instantaneous speed of flight of the vehicle, Representing the perceived power of the light, Which is indicative of the transmit power, The energy consumption coefficient of the chip is represented, Representing the calculated frequency.
8. The unmanned aerial vehicle general sensing computing resource allocation method for covering path tasks according to claim 1, wherein in S3, according to the energy consumption model, an expression for constructing a weighted cost function is: ; Wherein, the Representing a weighted cost function of the model, Indicating the total length of the path, The time-weighting coefficients are represented by a number of time-weighted coefficients, The energy weighting coefficients are represented by a set of coefficients, Indicating the position of the unmanned plane in the path The total instantaneous power at which the power is calculated, Representing the instantaneous flight speed.
9. The unmanned aerial vehicle sensory computing resource allocation method for an overlay path task of claim 1, wherein the plurality of constraints comprise perceived quality constraints, communication rate constraints, computational power constraints, physical resource constraints, and global energy hard constraints.
10. A drone passable computing resource allocation system for covering a path task, for implementing a drone passable computing resource allocation method for covering a path task as claimed in any one of claims 1 to 9, comprising: The deployment module is used for constructing a topology model of the target area according to the geographic information of the target area and deploying the ground user terminals in the geographic range covered by the topology model; The model construction module is used for constructing a perception quality model of the unmanned aerial vehicle, a communication rate model between the unmanned aerial vehicle and the ground user terminal, a calculation model of the data generation rate of the unmanned aerial vehicle and an energy consumption model of the total instantaneous power of the unmanned aerial vehicle; the optimization decision module is used for constructing a weighted cost function according to the energy consumption model, taking the weighted cost function as an optimization target, determining a decision variable to be optimized according to the optimization target, and determining a plurality of constraint conditions according to the decision variable to be optimized, the perceived quality model, the communication rate model and the calculation model; The path planning module is used for acquiring all nodes with odd degrees according to the topology model, constructing a first set, constructing a mixed weight complete graph according to the first set, calculating the weight of the mixed weight complete graph edge, carrying out augmentation on an undirected graph of the topology model according to the mixed weight complete graph and the weight to obtain an augmented undirected graph, and obtaining the total length of an optimal path according to the augmented undirected graph; The general sense calculation resource allocation module is used for acquiring an optimal communication power closed solution, an optimal perception power closed solution and an optimal calculation frequency closed solution according to the constraint conditions, obtaining an instantaneous minimum power function according to the optimal communication power closed solution, the optimal perception power closed solution, the optimal calculation frequency closed solution and the energy consumption model, calculating an optimal multiplier according to the instantaneous minimum power function and the total length of the optimal path, calculating the optimal speed of the unmanned aerial vehicle according to the optimal multiplier, and generating optimal perception power, optimal communication power and optimal calculation frequency according to the optimal speed to finish the general sense calculation resource allocation of the unmanned aerial vehicle.

Description

Unmanned aerial vehicle general sense computing resource allocation method and system for path task coverage Technical Field The invention relates to the technical field of wireless communication, in particular to a method and a system for distributing communication computing resources of unmanned aerial vehicle for covering path tasks. Background With the rapid development of low-altitude economy, an Unmanned AERIAL VEHICLE (abbreviated as a UAV) has become a core execution carrier for regional coverage and periodic inspection tasks by virtue of the advantages of strong maneuverability, flexible deployment and wide coverage. In applications such as intelligent agriculture, traffic road network inspection, pipeline and infrastructure monitoring, unmanned aerial vehicle needs to complete continuous coverage type perception of linear or planar target areas along a preset or dynamically adjusted flight path, and meanwhile, communication connection is provided for ground terminals, and real-time or quasi-real-time calculation processing is performed on acquired perception data or communication data, so that integrated service requirements of perception, communication and calculation are formed. To improve the comprehensive service capability of the unmanned aerial vehicle system, a general sense calculation integrated (INTEGRATED SENSING, communication and Computation, abbreviated as ISCC) technology has been developed. The technology integrates the functions of perception, communication and calculation on the same unmanned plane platform, relies on uniform system design and resource scheduling, and realizes the cooperative execution of multiple tasks. In an ISCC unmanned aerial vehicle system, the flight process needs to synchronously complete coverage sensing, ground user communication service and local/cooperative computing processing, the running performance of the system is highly dependent on the cooperative matching of a flight control strategy, a general sensing computing resource allocation mode and a path planning scheme, and the overall performance of the system is reduced due to the optimization and the disconnection of any link. Aiming at the Problem of unmanned aerial vehicle coverage path planning, the prior art scheme abstracts the unmanned aerial vehicle coverage path planning into graph theory or geometric coverage problems, such as path modeling based on a carrier Problem (TRAVELING SALESMAN Problem, abbreviated as TSP) and a Chinese postman Problem (Chinese Postman Problem, abbreviated as CPP), and optimizing targets are focused on minimizing flight distance or task completion time. However, such methods are optimized only from path geometry, default robots have fixed or sufficient sensing, communication, computing capabilities, and do not take into account the dynamic impact of changes in flight conditions such as speed, steering actions, etc. on the general computing performance, resulting in a planned path that is difficult to match with resource supply capabilities in actual execution. On the other hand, for research on unmanned aerial vehicle communication or computing power optimization, most of the unmanned aerial vehicle communication or computing power optimization is assumed that the flight path is known or fixed, and the core focuses on resource scheduling problems such as communication power control, spectrum allocation, computing and unloading strategies and the like. Such methods ignore the coupling relationship between the flight control variables and the general sense computing resources, resulting in the designed resource allocation strategy being difficult to maintain optimal in actual flight execution. In addition, some studies attempt to jointly optimize the flight trajectory and communication or perceived performance, but generally involve only partial coupling of perception and communication, or employ a phased, layered decoupling strategy, i.e., completing the flight path planning first, and then performing general sense computing resource allocation under a fixed path. In the practical application scene of periodic coverage, multi-constraint coordination and real-time response, the processing mode is easy to cause global performance degradation, and has insufficient adaptability and robustness to dynamic environment. In summary, in the ISCC unmanned aerial vehicle coverage path planning scene in the prior art, the common problems include that firstly, the flight control, coverage path planning and general sense computing resource allocation lack of a unified modeling and joint optimization framework, so that the three are insufficient in cooperativity, secondly, the existing model cannot accurately describe the comprehensive influence mechanism of the flight state change on the perceived quality, the communication performance and the computation time delay, and thirdly, the system optimization target is disjointed with engineering constraints such as unmanned aerial v