Cooperative control method and device for distributed computing and edge computing

Abstract

The invention relates to a cooperative control method and device for distributed computing and edge computing, and relates to the field of unmanned aerial vehicles. The method comprises the steps that the total calculation burden of a target unmanned aerial vehicle is acquired; acquiring first time of local calculation of the total calculation burden and second time of calculation of the total calculation burden at the edge calculation node; comparing the first time with the second time, and determining a task allocation strategy of the target unmanned aerial vehicle; and performing cooperative control based on a task allocation strategy. By adopting the method, the calculation tasks can be effectively distributed to different unmanned aerial vehicles and edge calculation nodes in a cluster in a mode of combining distributed calculation and edge calculation. In this way, the calculation pressure of a single unmanned aerial vehicle is relieved, and the overall calculation efficiency of large-scale task processing is remarkably improved. Especially for complex path planning, obstacle avoidance decision making and multi-target cooperation tasks, edge calculation can provide powerful calculation support at the position close to the unmanned aerial vehicle, and more efficient task execution is achieved.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and in particular to a method and device for collaborative control of distributed computing and edge computing. Background Art

[0002] With the rapid development of drone technology, drone swarm formations are widely used in military, logistics, emergency rescue and other fields.

[0003] However, in the coordinated control of large-scale drone swarms, each drone in the swarm not only needs to maintain communication with other drones, but also needs to calculate its flight trajectory, path planning, obstacle avoidance strategy, etc. in real time according to mission requirements. Traditional centralized computing architectures have problems such as data processing delays and excessive computing load when facing large-scale tasks, and cannot meet the real-time requirements of drone swarms. Summary of the invention

[0004] Based on this, it is necessary to provide a collaborative control method and device of distributed computing and edge computing that can improve the real-time performance of collaborative control of drone clusters in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for collaborative control of distributed computing and edge computing. The method includes:

[0006] Obtain the total computing burden of the target UAV; the total computing burden includes local computing tasks and network collaborative computing tasks, the local computing tasks are local computing tasks of the target UAV, and the network collaborative computing tasks are computing tasks distributed to other UAVs in the UAV cluster that are connected to the target UAV for communication;

[0007] Obtain a first time when the total computing load is calculated locally, and a second time when the total computing load is calculated at the edge computing node; compare the first time and the second time to determine a task allocation strategy for the target UAV;

[0008] The UAV cluster where the target UAV is located is collaboratively controlled based on the task allocation strategy.

[0009] In one embodiment, obtaining the total computational burden for the first time when the target drone is locally computed includes:

[0010] Obtain the local computing time based on the local computing task and the computing capability of the target UAV;

[0011] Acquire the collaborative computing time according to the network collaborative computing task, the computing capability of the drone receiving the network collaborative computing task, and the transmission time of the task data volume corresponding to the network collaborative computing task;

[0012] Get the first time based on the local computing time and the collaborative computing time.

[0013] In one embodiment, obtaining the total computing burden at the second time of computing at the edge computing node includes:

[0014] The second time is obtained according to the total computing burden, the computing power of the edge computing node receiving the total computing burden, and the transmission time of the task data volume corresponding to the total computing burden.

[0015] In one embodiment, 6G communication modules are used for data exchange between the target UAV and the communication-connected UAVs, and between the target UAV and the edge computing node.

[0016] In one embodiment, the method further includes: using a multi-level collaborative decision-making mechanism to perform collaborative control of the drone cluster;

[0017] Among them, the multi-level collaborative decision-making mechanism includes local decision-making with a single drone as the decision-making object, cluster decision-making with a sub-cluster composed of several drones as the decision-making object, and global decision-making with a drone swarm as the decision-making object.

[0018] On the second aspect, the present application also provides a collaborative control device for distributed computing and edge computing.

[0019] The device includes:

[0020] The data acquisition module is used to obtain the total computing burden of the target UAV; the total computing burden includes local computing tasks and network collaborative computing tasks. The local computing tasks are local computing tasks of the target UAV, and the network collaborative computing tasks are computing tasks distributed to other UAVs in the UAV cluster that are connected to the target UAV for communication;

[0021] The task allocation module is used to obtain a first time when the total computing burden is calculated locally and a second time when the total computing burden is calculated at the edge computing node; compare the first time and the second time to determine the task allocation strategy for the target UAV;

[0022] The collaborative control module is used to collaboratively control the drone cluster where the target drone is located based on the task allocation strategy.

[0023] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the above-mentioned collaborative control method of distributed computing and edge computing when executing the computer program.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned method for collaborative control of distributed computing and edge computing when executed by a processor.

[0025] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps in the above-mentioned method for collaborative control of distributed computing and edge computing when executed by a processor.

[0026] In a sixth aspect, the present application further provides a combined attack method based on the coordinated control of distributed computing and edge computing. Based on the above-mentioned coordinated control method of distributed computing and edge computing, the combined attack method includes:

[0027] Obtain mission requirements, space environment and drone state vector in real time; mission requirements include target relative position and target point position, and space environment includes obstacle position;

[0028] Obtain consistent control law based on the state vector and the target relative position;

[0029] Based on the state vector, a convergence attraction force is generated around the target point position, and a convergence repulsion force is generated around the obstacle position and the adjacent UAV. The convergence control law is obtained according to the convergence attraction and convergence repulsion forces.

[0030] Based on the state vector, a split repulsive force is generated around the center position of the drone cluster, and the split repulsive force and the combined repulsive force are used to obtain the split control law.

[0031] According to the consistency control law, the merging control law and the split-attack control law, an optimization model with the goal of optimizing the task execution efficiency is constructed. The formation and merging-split-attack strategies of the UAV cluster are obtained by solving the optimization model.

[0032] The above-mentioned collaborative control method and device of distributed computing and edge computing effectively distributes computing tasks to different drones and edge computing nodes in the cluster by combining distributed computing and edge computing. This not only reduces the computing pressure of a single drone, but also significantly improves the overall computing efficiency of large-scale task processing. In particular, for complex path planning, obstacle avoidance decisions, and multi-target collaborative tasks, edge computing can provide powerful computing support at a location close to the drone, achieving more efficient task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 1 is a flow chart of a method for collaborative control of distributed computing and edge computing in one embodiment;

[0034] Figure 2 This is a structural block diagram of a collaborative control device for distributed computing and edge computing in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] The computing tasks of existing drone clusters mainly rely on the local processor of each drone. This limitation is particularly prominent in large-scale complex tasks. Most drones have limited computing power due to power consumption, volume and other limitations, making it difficult to handle complex path planning, dynamic obstacle avoidance and multi-target collaborative combat tasks. Although some studies have attempted to introduce distributed computing and edge computing, the existing distributed computing architecture has not been fully optimized, and the coordination of task allocation and computing resources is insufficient, resulting in low cluster collaboration efficiency.

[0037] In view of the above problems, the present application provides a method for collaborative control of distributed computing and edge computing, such as Figure 1 As shown, the following steps are included:

[0038] Step 102, obtain the total computing burden of the target UAV; the total computing burden includes local computing tasks and network collaborative computing tasks, the local computing tasks are local computing tasks of the target UAV, and the network collaborative computing tasks are computing tasks distributed to other UAVs in the UAV cluster that are connected to the target UAV for communication.

[0039] In the distributed computing model, considering that each drone in the drone cluster is both a computing node and a task executor, the total computing burden of each drone to perform a task consists of two parts: local computing tasks and network collaborative computing tasks.

[0040] Let the N drones in the drone cluster be represented as U 1 ,U 2 ,…,U N , each drone U i The computational requirements of the local computing task can be expressed as:

[0041] C local,i =f(x i (t),u i (t))

[0042] Where: C local,i is the local computing task of UAV i; x i (t) is the state vector of UAV i at time t, including its position, velocity, acceleration, etc.; u i (t) is the control input of UAV i at time t.

[0043] In order to reduce the local computing burden, distributed computing introduces a collaborative mechanism, namely, UAV iPart of the computing tasks can be distributed to other UAVs for computing, and the total burden of network collaborative computing tasks is defined as C coop,i In an ideal situation, the UAV i The total computational burden can be expressed by the following formula:

[0044]

[0045] Where: C total,i is the total computational burden of UAV i; It's a drone i The neighbor set of C (i.e., the set of drones that can perform collaborative computing via wireless networks); coop,ij It is the task that UAV i delegates to UAV j for calculation.

[0046] Step 104, obtaining a first time when the total computing burden is calculated locally, and a second time when the total computing burden is calculated at the edge computing node; comparing the first time and the second time, determining a task allocation strategy for the target UAV.

[0047] For the total computing burden, the first time of local computing is obtained, including the time cost of local computing tasks on the target drone and the time cost of network collaborative computing tasks on the task receiving drone. At the same time, for the total computing cost, the second time of computing on the edge computing node is obtained.

[0048] Compare the first time and the second time, select the smaller one, and use the corresponding calculation method as the calculation method of the target drone for the total calculation burden, so as to achieve effective utilization of computing power, improve data processing speed, and thus improve the real-time performance of drone mission execution.

[0049] Step 106: Coordinated control of the drone cluster where the target drone is located is performed based on the task allocation strategy.

[0050] Collaborative control based on distributed computing and edge computing is mainly reflected in the overall task execution and decision optimization of the UAV cluster. Each UAV in the cluster must make autonomous decisions based on its own local information, and optimize the action strategy of the entire cluster through communication and collaboration with other UAVs.

[0051] The cooperative control problem of UAV swarm can be modeled as the optimal control problem of a multi-agent system, where each UAV U i The control goal is to minimize its local cost function J i (x i (t),u i (t)), the cost function is related to factors such as the UAV’s energy consumption, mission completion, and path planning.

[0052] The global collaborative control objective can be expressed as the overall cost function J of the entire cluster: total :

[0053]

[0054] Where: ij is the coordination weight between neighboring UAVs, which controls the coordination degree between each UAV. The coordination control law can be derived from the overall cost function J by gradient descent method. total It is derived from the above to obtain the optimal control input u for each UAV. i (t):

[0055]

[0056] K i Denotes the control gain matrix. The control law shows that each drone must not only perform local optimization based on its own mission status, but also exchange information with neighboring drones to make collaborative decisions and adjust paths, thereby optimizing the overall action strategy of the cluster. By combining distributed computing with edge computing, the collaborative control of drone clusters has been greatly optimized. Distributed computing improves the autonomous decision-making ability of each drone and reduces the delay problem caused by centralized computing by distributing computing tasks to drone clusters; the introduction of edge computing provides a guarantee for real-time processing of complex tasks through powerful computing power and low-latency communication support.

[0057] In one embodiment, obtaining the first time when the total computing burden is calculated locally on the target UAV includes: obtaining the local computing time based on the local computing task and the computing power of the target UAV; obtaining the collaborative computing time based on the network collaborative computing task, the computing power of the UAV receiving the network collaborative computing task, and the transmission time of the task data volume corresponding to the network collaborative computing task; obtaining the first time based on the local computing time and the collaborative computing time.

[0058] For each drone U i For example, the computational burden C total,i It is necessary to optimize between local computing tasks and network collaborative computing tasks. The effect of network collaborative computing tasks depends on the communication bandwidth and delay between drones. Therefore, considering the communication delay τ ij (t) and bandwidth B ij The transmission time of the collaborative computing task can be expressed as:

[0059]

[0060] Where: D coop,ij is the amount of task data that UAV i assigns to UAV j for calculation; B ijis the communication bandwidth between UAVs i and j; τ ij (t) is the communication delay between UAVs i and j at time t. i The total task execution time T total,i It can be expressed as the sum of local computing time and collaborative computing time, that is, the first time is expressed as:

[0061]

[0062] Where: f local,i is the local computing capacity of UAV i; f coop,j is the collaborative computing capability of UAV j.

[0063] In other embodiments, in order to minimize the local task execution time of the entire cluster, it is necessary to allocate the computing tasks of each drone through an optimization algorithm. The optimization goal can be expressed as:

[0064]

[0065] This optimization problem can be solved by distributed optimization algorithms, such as gradient descent or Lagrange multiplier method, to dynamically adjust the task allocation between drones to achieve optimal collaborative computing performance.

[0066] In one embodiment, obtaining the second time when the total computing burden is calculated at the edge computing node includes: obtaining the second time based on the total computing burden, the computing power of the edge computing node receiving the total computing burden, and the transmission time of the task data volume corresponding to the total computing burden.

[0067] Although distributed computing can effectively disperse computing tasks, when the task complexity is high, collaborative computing between drones alone may still not meet real-time requirements. For this reason, edge computing is introduced, which offloads part of the computing tasks to edge computing nodes close to drones, such as edge servers of 6G base stations. This architecture can significantly improve computing power while maintaining low communication latency.

[0068] For UAV i For example, the task offloading model of edge computing can be expressed as:

[0069]

[0070] Where: D offload,i It's a drone i The amount of task data that needs to be offloaded to the edge computing node; R edge,i It's a drone i Communication rate with edge nodes; C edge,i is the amount of computing tasks that the edge computing node needs to process; f edgeIt is the computing power of the edge computing node. i The total task execution time can be optimized as follows:

[0071]

[0072] This shows that drones can choose the optimal strategy of local computing, collaborative computing or task offloading according to the current network status, task complexity and load of edge computing nodes to ensure that the task is completed in the shortest time.

[0073] In one embodiment, 6G communication modules are used for data exchange between the target UAV and the communication-connected UAVs, and between the target UAV and the edge computing node.

[0074] 6G communication technology has been used to achieve higher communication bandwidth and lower latency. Compared with traditional 4G / 5G networks, 6G communication provides Tbps-level data transmission rates and millisecond-level ultra-low latency, which can effectively support the real-time communication needs of large-scale drone clusters. When drone clusters are working together or performing complex tasks, each drone can share status information, mission instructions, and perception data in real time, greatly improving the efficiency and reliability of information transmission, and ensuring that all drones in the cluster can complete task coordination and response within millisecond-level delays. This ultra-low latency communication ensures the real-time and accuracy of dynamic formation adjustment, path planning, and target tracking, and improves the efficiency of cluster collaboration.

[0075] The collaborative control method of distributed computing and edge computing proposed in this application combines the high bandwidth and low latency characteristics of the 6G communication network to ensure efficient communication and task allocation between drones. At the same time, the combination of distributed computing and edge computing greatly improves the overall computing power of the cluster, reduces the delay in task execution, and improves the efficiency and accuracy of task completion. In the multi-task scenarios of future drone clusters, this method has broad application prospects, especially in complex mission environments such as military, logistics distribution, and disaster relief, and can significantly improve the collaborative efficiency of drone clusters.

[0076] In one embodiment, the method also includes: adopting a multi-level collaborative decision-making mechanism to perform collaborative control of the drone cluster; wherein the multi-level collaborative decision-making mechanism includes local decision-making with a single drone as the decision-making object, cluster decision-making with a sub-cluster composed of several drones as the decision-making object, and global decision-making with a drone cluster as the decision-making object.

[0077] In the collaborative control and task execution process of large-scale drone swarms, a single-level control and decision-making mechanism is often unable to cope with the multi-task requirements in a complex dynamic environment. Drone swarms need to be able to make decisions at multiple levels, including autonomous decisions of individual drones, small-scale collaborative decisions of neighboring drones, and global decisions of the entire swarm. In the multi-level collaborative decision-making mechanism, decision-making tasks are divided into local decisions (local layer), cluster collaborative decisions (middle layer), and global decisions (top layer), and efficient operation of the swarm is achieved through collaboration at different levels.

[0078] The high-speed, low-latency communication and edge computing capabilities of 6G networks provide the basis for multi-level collaborative decision-making, enabling drone swarms to quickly share information and optimize decisions at different levels, ensuring efficient execution of tasks in complex and dynamic environments.

[0079] In order to effectively coordinate the decisions at different levels, a hierarchical optimization framework is introduced, which performs collaborative decision-making at three levels: local, regional, and global.

[0080] At the local level, each drone makes independent decisions based on its own perception information, mainly dealing with drone path planning, obstacle avoidance, and local task execution. The local decision-making goal of drone i can be expressed as a local optimization problem

[0081]

[0082] in: is the local objective function of UAV i, which may include multiple factors such as path planning, obstacle avoidance, and energy consumption; u i (t) is the control input of UAV i at time t; x i (t) is the state vector (including position, speed, etc.) of UAV i at time t. With the support of 6G network, local decision-making can obtain the perception information of UAVs and the status information of neighboring UAVs in real time to ensure the real-time performance of path planning and obstacle avoidance.

[0083] At the cluster level, a sub-cluster consisting of several drones makes collaborative decisions based on regional mission objectives to ensure efficient collaboration between drones within the cluster. The goal of this level is to optimize the execution of local collaborative tasks of the entire cluster, such as collective advance, target siege, joint detection, etc.

[0084] The cluster collaborative decision-making problem can be expressed as a distributed optimization problem:

[0085]

[0086] Among them: J cluster is the objective function of the cluster, which may include tasks such as target capture, task allocation, and joint detection; XC (t) = [x 1 (t),x 2 (t),…,x k (t)] is the state matrix of the UAVs in the cluster; U C (t)=[u 1 (t),u 2 (t),…,u k (t)] is the control input of the drones in the cluster; k represents the number of drones in the cluster. The collaborative decision-making of the cluster can be achieved through the "consensus control" mechanism. This mechanism enables each drone in the cluster to gradually tend to the shared decision results through communication between adjacent drones, that is, the control input of each drone depends not only on its own state, but also on the state of neighboring drones. The collaborative control law of the cluster can be expressed as:

[0087]

[0088] Where: K is the control gain matrix; is the neighbor set of drone i; g i (t) is the local task-driven control input of UAV i. This method ensures that the UAVs in the cluster maintain relative consistency when performing tasks, while being able to make individual adjustments based on local needs.

[0089] At the global level, the top-level command center or the global controller of the drone cluster is responsible for the strategic decision-making of the entire cluster, such as task allocation, target prioritization, resource allocation, etc. The goal of global decision-making is to coordinate all drones and sub-clusters to ensure the optimal completion of the entire cluster's tasks.

[0090] The global decision can be modeled as a multi-objective optimization problem:

[0091]

[0092] Among them: J global is the global objective function of the cluster, which may include task completion time, energy consumption, target capture rate, etc.; X(t) = [x 1 (t),x 2 (t),…,x N (t)] is the state matrix of the entire cluster; U(t) = [u 1 (t),u 2 (t),…,u N (t)] is the control input for the entire cluster.

[0093] Global decisions can be optimized through methods such as reinforcement learning or dynamic programming. At the global level, the edge computing nodes of the 6G network can provide powerful computing power to process the information flow from all drones in real time and make the best decision based on the global situation.

[0094] To achieve high efficiency of multi-level collaborative decision-making, we adopt a hierarchical optimization framework to ensure that decisions at all levels can cooperate and optimize each other through a top-down coordination mechanism and a bottom-up feedback mechanism. The global level determines the task allocation and priority of each subcluster according to the overall requirements of the task, and generates a preliminary task allocation matrix T through the global task planner. assign :

[0095]

[0096] in: Represents the set of all subclusters, the task allocation matrix T assign It is generated by optimizing the global goal of the cluster and the synergy of the sub-clusters. The cluster level is based on the task allocation matrix T of the global decision assign , combined with the local goals of the cluster, optimize its internal task execution and collaborative operations. Individual drones at the local level make autonomous adjustments based on the collaborative decision-making of the cluster and the information they perceive locally. Each drone will perform real-time path optimization and local goal correction in its local environment based on the decisions of neighboring drones and its independently perceived tasks.

[0097] The effective implementation of multi-level collaborative decision-making mechanisms depends on efficient communication and computing resource allocation within the drone cluster. The ultra-high-speed communication and edge computing capabilities of 6G networks provide support for multi-level collaborative decision-making, especially at the cluster collaboration and global decision-making levels where data transmission and computing requirements are high.

[0098] For communication resource allocation, the network slicing technology of 6G networks can dynamically adjust the communication bandwidth and latency according to the decision-making requirements at different levels. The global decision-making level requires higher bandwidth and lower latency to obtain global situation information, while the cluster level needs to ensure low latency and high reliability of communication to achieve efficient collaborative control. The optimization goal of communication resource allocation can be expressed as:

[0099]

[0100] Where: B(t) is the communication bandwidth allocation matrix of each level of decision; It is the communication delay at different levels; are the delay weight coefficients of different levels.

[0101] For computing resource allocation, the edge computing nodes of the 6G network dynamically allocate computing resources according to the computing needs of each level. Decisions at the global level require strong computing power to handle complex task planning and optimization problems, while drones at the local level mainly rely on their own computing resources for real-time path planning and control. Therefore, the optimization of computing resource allocation can be expressed by the following formula:

[0102]

[0103] Where: f edge (t) is the allocation matrix of edge computing resources; is the computation time of each level; is the weight coefficient for computing resource allocation.

[0104] The multi-level collaborative decision-making mechanism combines global strategic decision-making, cluster collaborative decision-making and individual autonomous decision-making through a hierarchical optimization framework, greatly improving the execution efficiency of drone clusters in complex tasks. The novelty and innovation of this mechanism are reflected in its realization of rapid response and efficient collaboration of drone clusters in different mission scenarios through hierarchical collaboration and optimization. In addition, the high-speed communication and edge computing capabilities of the 6G network provide strong technical support for multi-level collaborative decision-making, ensuring real-time and robustness in complex dynamic environments. This collaborative decision-making mechanism has broad application prospects, especially in the fields of military, disaster relief and logistics distribution, and can effectively improve the task execution efficiency and resource utilization of drone clusters.

[0105] In one embodiment, a combined attack and split attack method based on the collaborative control of distributed computing and edge computing is disclosed. Based on the above-mentioned distributed computing and edge computing collaborative control method, the combined attack and split attack method includes: real-time acquisition of task requirements, spatial environment and state vectors of drones; the task requirements include target relative position and target point position, and the spatial environment includes obstacle position; based on the state vector and target relative position, a consistency control law is acquired; based on the state vector, a combined attraction force is generated around the target point position, and a combined repulsion force is generated around the obstacle position and adjacent drones, and a combined control law is acquired according to the combined attraction and combined repulsion force; based on the state vector, a split repulsion force is generated around the center position of the drone cluster, and a split control law is acquired according to the split repulsion force and the combined repulsion force; based on the consistency control law, the combined control law and the split control law, an optimization model with the goal of optimizing task execution efficiency is constructed, and the formation and combined attack and split attack strategy of the drone cluster are acquired by solving the optimization model.

[0106] When drone swarms are performing complex tasks, they usually need to flexibly adjust their formations according to changes in the mission scenario. This dynamic formation requires drone swarms to be able to reorganize their formations in real time based on information such as the environment, target location, and threat situation, in order to achieve efficient target approach and attack (co-attack) or rapid dispersion after attack (separate attack), thereby achieving efficient mission execution and self-defense.

[0107] Traditional formation control methods mostly use fixed formation modes, which are difficult to cope with changing environments and mission requirements. Dynamic formation and converge and diverge strategies use intelligent algorithms to achieve flexible adjustments of drone formations, allowing clusters to efficiently aggregate during the target approach phase and quickly disperse after completing attacks or other tasks to avoid enemy counterattacks and enhance overall flexibility and autonomy.

[0108] For the consistency control law, the formation problem of the UAV swarm can be abstracted as a formation maintenance and adjustment problem of a multi-agent system. Let the number of UAVs in the swarm be N, and the state vector of each UAV be where p i (t), v i (t) and a i (t) represent the spatial position, velocity and acceleration of UAV i at time t, respectively.

[0109] The dynamic formation control goal of the UAV swarm can be expressed as: at any time t, the relative positions between the UAVs must satisfy the constraints of the formation. Define the expected relative position between UAVs i and j as d ij (t), then the control objective of the dynamic formation can be expressed as:

[0110]

[0111] where d ij (t) is the relative position of the dynamic target that changes with time, which is used to adapt to different mission scenarios. The dynamic formation goal of the UAV cluster is to enable each UAV to adjust its position and speed according to the state of the neighboring UAVs, so as to ensure that the formation of the entire cluster is maintained.

[0112] The consistency control law can be expressed as:

[0113]

[0114] Where: u i (t) is the control input (acceleration or velocity adjustment) of UAV i; K is the control gain matrix used to adjust the stability of the formation; is the neighbor set of UAV i (i.e., the UAVs with which it has direct communication links).

[0115] Through the consistency control law, the UAVs can maintain the formation by adjusting the relative positions with neighboring UAVs, and at the same time, the relative position d ij (t) changes to achieve dynamic adjustment of the formation. For example, during the target approach phase, the drone cluster can gradually aggregate from a dispersed formation to an attack formation, and then quickly disperse after completing the attack.

[0116] For the convergence control law, the goal of the convergence strategy is to quickly gather the drone cluster near the target and form a siege situation during the attack phase to achieve concentrated firepower or task execution. The convergence strategy generates an attractive potential field for the target and a repulsive potential field for threats or obstacles to guide the drone cluster to converge toward the target.

[0117] Define the target position as p target (t), the attraction of UAV i can be expressed as:

[0118]

[0119] Among them: U att,i (t) is the attractive potential of UAV i; k att is the gain coefficient of the attractive potential; p i (t is the position of drone i; p target (t) is the position of the target.

[0120] To avoid collisions between drones or with other obstacles, the repulsive potential field needs to be considered during the merging process. The position of obstacles or other drones is defined as p obs (t), then the repulsive force on UAV i is:

[0121]

[0122] Where: k rep is the gain coefficient of the repulsive potential; ||p i (t)-p j (t)|| is the distance between drones i and j.

[0123] The combined control law of UAV i can be expressed as the resultant force of attraction and repulsion:

[0124] u i (t) = F att,i (t)+F rep,i (t)

[0125] This control law ensures that the drones can gradually gather to form a siege situation when approaching the target, and maintain a certain distance while avoiding collision to achieve coordinated attack.

[0126] For the split-attack control law, the split-attack strategy is to quickly disperse the drone cluster after the combined attack is completed to prevent the enemy's counterattack or interference. The split-attack strategy requires the drone cluster to be able to leave the battlefield as soon as possible after completing the attack mission and enter a dispersed defense posture. The split-attack strategy can be implemented through a potential field method similar to the combined strategy, but unlike the combined strategy, the drone cluster is no longer attracted by the target during the split-attack, but is affected by the repulsive potential field to maintain the dispersion and maneuverability of the cluster.

[0127] Define the center position p center (t) is the center of mass of the drone cluster. The repulsive force on drone i during the attack process can be expressed as:

[0128]

[0129] Where: k split is the gain coefficient of the scattered potential field; It is the center of the drone cluster.

[0130] In this way, the drone cluster will quickly leave the central area after completing the mission and maintain a safe distance from neighboring drones to avoid being counterattacked by the enemy. The overall attack control law can be expressed as:

[0131]

[0132] This control law ensures that the drones remain dispersed during the strike process while avoiding collisions with each other.

[0133] In one embodiment, the execution of the merge-and-divide strategy needs to be dynamically optimized according to real-time mission requirements and environmental changes. With the support of the 6G communication network, drones can obtain information about neighboring drones and the environment through a low-latency communication network, and perform dynamic formation adjustments and optimization of the merge-and-divide strategy based on this information.

[0134] The overall task execution efficiency E(t) of the drone cluster can be expressed as:

[0135]

[0136] Where: T complete,i (t) is the time it takes for UAV i to complete the task; E energy,i (t) is the energy consumption of UAV i; α i It is the weight coefficient of energy consumption, which is used to balance the relationship between task completion time and energy consumption.

[0137] The optimization goal is to minimize the total task completion time and reduce energy consumption. This problem can be solved by real-time dynamic programming or reinforcement learning algorithms to ensure that the drone cluster achieves optimal efficiency when executing the merge-and-divide strategy. The dynamic formation and merge-and-divide strategy of the present invention ensure that the drone cluster can flexibly adjust the formation and coordinate attacks according to mission requirements. Through the merge strategy, the drone cluster can efficiently gather near the target for attack; through the divergent strategy, the cluster can quickly disperse after the attack to avoid enemy counterattacks. The innovation of this method is reflected in the combination of the real-time communication capabilities of the 6G network, which improves the dynamic response capabilities of the drone cluster, ensures the efficient execution and autonomous defense of the cluster in complex tasks, and has significant practicality and application prospects.

[0138] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0139] Based on the same inventive concept, the embodiment of the present application also provides a distributed computing and edge computing collaborative control device for implementing the above-mentioned distributed computing and edge computing collaborative control method. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more distributed computing and edge computing collaborative control device embodiments provided below can be found in the above-mentioned limitations on the distributed computing and edge computing collaborative control method, which will not be repeated here.

[0140] In one embodiment, Figure 2 As shown, a collaborative control device for distributed computing and edge computing is provided, including: a data acquisition module 202, a task allocation module 204 and a collaborative control module 206, wherein:

[0141] The data acquisition module 202 is used to obtain the total computing burden of the target UAV; the total computing burden includes local computing tasks and network collaborative computing tasks, the local computing tasks are local computing tasks of the target UAV, and the network collaborative computing tasks are computing tasks distributed to other UAVs in the UAV cluster that are connected to the target UAV for communication;

[0142] The task allocation module 204 is used to obtain a first time when the total computing burden is calculated locally and a second time when the total computing burden is calculated at the edge computing node; compare the first time and the second time to determine a task allocation strategy for the target UAV;

[0143] The collaborative control module 206 is used to perform collaborative control on the drone cluster where the target drone is located based on the task allocation strategy.

[0144] Each module in the above-mentioned distributed computing and edge computing collaborative control device can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0145] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in all the above method embodiments when executing the computer program.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in all the above method embodiments are implemented.

[0147] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in all the above method embodiments when executed by a processor.

[0148] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0150] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for collaborative control of distributed computing and edge computing, characterized in that: The method comprises: Obtaining the total computing burden of the target UAV; the total computing burden includes local computing tasks and network collaborative computing tasks, the local computing tasks are local computing tasks of the target UAV, and the network collaborative computing tasks are computing tasks distributed to other UAVs in the UAV cluster that are in communication connection with the target UAV; Obtaining a first time at which the total computing burden is locally calculated, and a second time at which the total computing burden is calculated at an edge computing node; comparing the first time and the second time to determine a task allocation strategy for the target UAV; Based on the task allocation strategy, the drone cluster where the target drone is located is collaboratively controlled.

2. The method according to claim 1, characterized in that The obtaining of the total computing burden at the first time of the local computing of the target UAV includes: Obtaining local computing time according to the local computing task and the computing capability of the target UAV; Acquire the collaborative computing time according to the network collaborative computing task, the computing capability of the drone receiving the network collaborative computing task, and the transmission time of the task data volume corresponding to the network collaborative computing task; The first time is acquired according to the local computing time and the collaborative computing time.

3. The method according to claim 1, characterized in that Obtaining the second time at which the total computing burden is calculated at the edge computing node includes: The second time is obtained according to the total computing burden, the computing capability of the edge computing node receiving the total computing burden, and the transmission time of the task data volume corresponding to the total computing burden.

4. The method according to claim 1, characterized in that: The 6G communication module is used for data exchange between the target UAV and the communication-connected UAV, and between the target UAV and the edge computing node.

5. The method according to claim 1, characterized in that The method further comprises: adopting a multi-level collaborative decision-making mechanism to perform collaborative control of the drone cluster; Among them, the multi-level collaborative decision-making mechanism includes local decision-making with a single drone as the decision-making object, cluster decision-making with a subcluster composed of several drones as the decision-making object, and global decision-making with the drone group as the decision-making object.

6. A collaborative control device for distributed computing and edge computing, characterized in that: The device comprises: A data acquisition module is used to obtain the total computing burden of the target UAV; the total computing burden includes local computing tasks and network collaborative computing tasks, the local computing tasks are local computing tasks of the target UAV, and the network collaborative computing tasks are computing tasks distributed to other UAVs in the UAV cluster that are in communication connection with the target UAV; A task allocation module, configured to obtain a first time at which the total computing burden is locally calculated, and a second time at which the total computing burden is calculated at an edge computing node; and to compare the first time with the second time to determine a task allocation strategy for the target UAV; A collaborative control module is used to collaboratively control the drone cluster where the target drone is located based on the task allocation strategy.

7. A method for coordinated control of distributed computing and edge computing based on the task distributed computing and edge computing according to any one of claims 1 to 5, the method comprising: Obtain mission requirements, space environment, and drone state vectors in real time; The task requirements include the relative position of the target and the position of the target point, and the spatial environment includes the position of obstacles; Acquire a consistency control law based on the state vector and the target relative position; Based on the state vector, a merging attraction force is generated around the target point position, a merging repulsion force is generated around the obstacle position and the adjacent UAV, and a merging control law is obtained according to the merging attraction force and the merging repulsion force; Based on the state vector, a split repulsive force is generated around the center position of the drone cluster, and a split repulsive force control law is obtained according to the split repulsive force and the combined repulsive force; According to the consistency control law, the merging control law and the split-attack control law, an optimization model with the goal of optimizing task execution efficiency is constructed, and the formation and merging-split-attack strategy of the drone cluster are obtained by solving the optimization model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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