An unmanned aerial vehicle task computing flight strategy optimization method and system

Abstract

The application discloses a kind of unmanned vehicle task computing flight strategy optimization method and system, to solve the technical problem that the processing cost of existing unmanned vehicle task computing flight strategy optimization technology leads to computing task is too high.Method includes determining task transmission unloading rate based on the user coordinate data and unmanned vehicle coordinate data obtained;Based on the preset unloading decision constraint, the unmanned vehicle state data obtained, task transmission unloading rate and the user computing task data obtained, determine target task completion delay and target task completion energy consumption, combined with the preset unloading decision constraint, the preset unmanned vehicle constraint, user unmanned vehicle unloading data and unmanned vehicle coordinate data, construct joint optimization problem;The joint optimization problem is converted, and the multi-agent Markov decision process model is determined;Multi-agent deep reinforcement learning algorithm is used to optimize and solve the multi-agent Markov decision process model, and the target unmanned vehicle task computing flight strategy is generated.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for optimizing unmanned aerial vehicle (UAV) mission calculation flight strategy. Background Art

[0002] In recent years, drone technology has experienced rapid development. Due to its advantages, such as small size, maneuverability, ease of deployment, high adaptability, and high probability of line-of-sight links, it has gained widespread attention not only in the military but also in the civilian sector. Drone applications have made significant progress in disaster relief, communication assistance, and network coverage.

[0003] Drones can hover and fly. As high-altitude mobile platforms, they can carry mobile edge computing servers, addressing the challenges of traditional mobile edge computing deployment and providing more flexible and efficient computing and communication services. Drones, particularly in remote areas and disaster zones, can be quickly deployed as MEC (Multi-access Edge Computing) nodes, ensuring continuity of network connectivity and data processing. This significantly improves emergency response capabilities and network service quality, thereby providing timely and reliable communication and computing services.

[0004] Most existing drone mission calculation flight strategy optimization technologies consider that a task can be performed locally by the user or offloaded to the drone for calculation. The user side considers how to offload the subtask. However, simultaneous offloading and calculation by the user side consumes a lot of energy, resulting in excessively high processing costs for the computing task. Summary of the Invention

[0005] The present invention provides a method and system for optimizing the flight strategy of a UAV mission calculation, which are used to solve the technical problem that the processing cost of computing tasks is too high due to the existing UAV mission calculation flight strategy optimization technology.

[0006] A first aspect of the present invention provides a method for optimizing a UAV mission calculation flight strategy, comprising:

[0007] Obtain user computing task data, user coordinate data, drone status data, and drone coordinate data;

[0008] Determining user-drone distance data based on the user coordinate data and the drone coordinate data, and determining a task transmission offloading rate based on the user-drone distance data and drone altitude data in the drone coordinate data;

[0009] Based on the preset offloading decision constraints, determine the user drone offloading data, and determine the target task completion delay and target task completion energy consumption according to the user drone offloading data, the drone status data, the task transmission offloading rate, and the computing task complexity and computing task size in the user computing task data;

[0010] A joint optimization problem is constructed using the preset offloading decision constraints, the target task completion delay, the target task completion energy consumption, preset drone constraints, the user drone offloading data, and the drone coordinate data;

[0011] Transforming the joint optimization problem to determine a multi-agent Markov decision process model;

[0012] A multi-agent deep reinforcement learning algorithm is used to optimize and solve the multi-agent Markov decision process model to generate a target UAV mission calculation flight strategy.

[0013] Optionally, the drone coordinate data includes three-dimensional cluster drone coordinates and three-dimensional calculated drone coordinates; the user drone distance data includes distance data between the user and the cluster drone, distance data between the user and the calculated drone, and distance data between drones; and determining the user drone distance data based on the user coordinate data and the drone coordinate data includes:

[0014] Performing Euclidean distance calculations on the three-dimensional user coordinates in the user coordinate data, the three-dimensional cluster drone coordinates, and the three-dimensional calculation drone coordinates, and outputting distance data between the user and the cluster drone, and distance data between the user and the calculation drone;

[0015] The distance data between the drones is determined based on the three-dimensional cluster drone coordinates and the three-dimensional calculated drone coordinates.

[0016] Optionally, the task transmission offloading rate includes a user-UAV transmission rate and a task offloading rate between UAVs; and determining the task transmission offloading rate based on the user-UAV distance data and the UAV height data in the UAV coordinate data includes:

[0017] Determining a connection probability between the user and the drone based on the distance data between the user and the cluster drone, the distance data between the user and the calculation drone, and the drone height data in the drone coordinate data;

[0018] Determine the link channel power gain between the user and the drone based on the connection probability between the user and the drone, the distance data between the user and the cluster drone, and the distance data between the user and the computing drone;

[0019] The preset Shannon formula is used to determine the transmission rate between the user and the UAV according to the power gain of the link channel between the user and the UAV;

[0020] Based on the distance data between the UAVs, a task offloading rate between the UAVs is determined.

[0021] Optionally, the computing task complexity includes computing the computing task complexity of a UAV and the computing task complexity of a cluster UAV; the computing task size includes computing the computing task size of a UAV and the computing task size of a cluster UAV; the user-UAV transmission rate includes the user-computing UAV transmission rate and the user-cluster UAV transmission rate; and determining the target task completion delay and the target task completion energy consumption based on the user UAV unloading data, the UAV status data, the task transmission unloading rate, and the computing task complexity and computing task size in the user computing task data includes:

[0022] According to the computing task complexity of the computing drone, the computing task size of the computing drone, and the transmission rate between the user and the computing drone, the completion delay of the user offloading computing drone task and the energy consumption of the user offloading computing drone task are determined;

[0023] Determine the completion delay of the user unloading swarm drone task and the energy consumption of the user unloading swarm drone task based on the user drone unloading data, the complexity of the swarm drone computing task, the size of the swarm drone computing task, and the transmission rate between the user and the swarm drone;

[0024] Determine the target task completion delay based on the drone status data, the user drone unloading data, the user unloading calculation drone task completion delay, and the fixed user unloading cluster drone task completion delay;

[0025] The target task completion energy consumption is determined based on the drone status data, the user drone unloading data, the user unloading calculation drone task completion energy consumption and the user unloading cluster drone task completion energy consumption.

[0026] Optionally, constructing a joint optimization problem by using the preset offloading decision constraints, the target task completion delay, the target task completion energy consumption, preset drone constraints, the user drone offloading data, and the drone coordinate data includes:

[0027] Determining a task completion cost based on the target task completion delay and the target task completion energy consumption;

[0028] A joint optimization problem is constructed using the preset offloading decision constraints, the task completion cost, the preset drone constraints, the user drone offloading data, and the drone coordinate data.

[0029] Optionally, the user drone unloading data includes first unloading data, second unloading data, third unloading data, and fourth unloading data; the preset unloading decision constraints include first unloading decision constraints, second unloading decision constraints, third unloading decision constraints, fourth unloading decision constraints, and fifth unloading decision constraints; the preset drone constraints include first drone constraints, second drone constraints, third drone constraints, fourth drone constraints, fifth drone constraints, sixth drone constraints, seventh drone constraints, and eighth drone constraints; the drone coordinate data also includes two-dimensional cluster drone coordinates; the joint optimization problem is specifically:

[0030]

[0031] Where P is a joint optimization problem; is the two-dimensional cluster UAV coordinate, which represents the optimization variable of the cluster UAV trajectory. , is the horizontal coordinate of the mth swarm UAV at time slot t, is the vertical coordinate of the mth cluster UAV at time slot t; B is the user UAV unloading data, which represents the unloading decision variables of the user and cluster UAV, , The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the uth user offloads the task to the nth computing drone for calculation at time slot t. The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When t is t, the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t. The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When t is t, the mth cluster drone performs local calculation on the task at time slot t. The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When , the mth swarm drone performs local computation on the task at time slot t, where M is the set of swarm drones; is the task completion cost at time slot t; T is the time slot set; C1 is the first offloading decision constraint, indicating that the task of each ground user (user) can only be offloaded to a cluster drone or a computing drone, and U is the set of users; C2 is the second offloading decision constraint, indicating the constraint of the offloading decision variables calculated locally by the cluster drone; C3 is the third offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to the computing drone, and N is the set of computing drones; C4 is the fourth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to another cluster drone; C5 is the fifth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone; C6 is the first drone constraint, indicating the mobility constraint of the service range of each cluster drone. The maximum length of the service area divided by the mth cluster drone, is the maximum width of the service area divided by the mth swarm drone; C7 is the second drone constraint, which means that the ground user can offload the task to the coverage constraint of the swarm drone. is the two-dimensional user coordinate of the u-th user at time slot t, is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal coverage of the mth swarm UAV; C8 is the constraint of the third UAV, which means the constraint to avoid collision between swarm UAVs. is the three-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional coordinates of a cluster UAV at time slot t, is the minimum safety distance; C9 is the fourth drone constraint, which means the constraint to avoid overlapping coverage between cluster drones. is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional cluster UAV coordinates of the cluster UAV at time slot t; C10 is the fifth UAV constraint, which represents the constraint of the horizontal flight angle of the cluster UAV. is the horizontal flight angle of the mth swarm UAV at time slot t; C11 is the sixth UAV constraint, which represents the constraint on the horizontal flight distance of the swarm UAV. is the flight distance of the mth swarm drone at time slot t, is the maximum flight distance allowed by the swarm drone; C12 is the seventh drone constraint, indicating the state of the swarm drone. is the UAV status data of the mth cluster UAV at time slot t, when When , it means that the mth cluster drone is in a fault state at time slot t. C13 represents the eighth unmanned aerial vehicle constraint, indicating that the computing task needs to be completed before the maximum tolerable time delay under the t time slot, target task completion time delay, maximum tolerable delay time.

[0032] The second aspect of the present application provides an unmanned aerial vehicle task computing flight strategy optimization system, comprising:

[0033] An acquisition module is configured to acquire user computing task data, user coordinate data, unmanned aerial vehicle state data, and unmanned aerial vehicle coordinate data.

[0034] A determination module is configured to determine user unmanned aerial vehicle distance data based on the user coordinate data and the unmanned aerial vehicle coordinate data, and determine a task transmission offloading rate based on the user unmanned aerial vehicle distance data and unmanned aerial vehicle height data in the unmanned aerial vehicle coordinate data.

[0035] A time delay and energy consumption determination module is configured to determine user unmanned aerial vehicle offloading data based on preset offloading decision constraints, and determine target task completion time delay and target task completion energy consumption based on the user unmanned aerial vehicle offloading data, the unmanned aerial vehicle state data, the task transmission offloading rate, and computing task complexity and computing task size in the user computing task data.

[0036] A construction module is configured to construct a joint optimization problem by using the preset offloading decision constraints, the target task completion time delay, the target task completion energy consumption, preset unmanned aerial vehicle constraints, the user unmanned aerial vehicle offloading data, and the unmanned aerial vehicle coordinate data.

[0037] A conversion module is configured to convert the joint optimization problem to determine a multi-agent Markov decision process model.

[0038] A solution module is configured to optimize and solve the multi-agent Markov decision process model by using a multi-agent deep reinforcement learning algorithm to generate a target unmanned aerial vehicle task computing flight strategy.

[0039] The third aspect of the present application provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the unmanned aerial vehicle task computing flight strategy optimization method according to any one of the above aspects.

[0040] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the steps of the unmanned aerial vehicle task computing flight strategy optimization method according to any one of the above aspects.

[0041] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the drone mission calculation flight strategy optimization method as described in any one of the above items.

[0042] It can be seen from the above technical solutions that the present invention has the following advantages:

[0043] The above technical solution of the present invention provides a method for optimizing the flight strategy of UAV task calculation. First, user calculation task data, user coordinate data, UAV status data and UAV coordinate data are obtained; then, based on the user coordinate data and the UAV coordinate data, the user UAV distance data is determined, and the task transmission unloading rate is determined according to the user UAV distance data and the UAV height data in the UAV coordinate data; based on the preset unloading decision constraints, the user UAV unloading data is determined, and the target task completion delay and the target task completion energy consumption are determined according to the user UAV unloading data, the UAV status data, the task transmission unloading rate and the calculation task complexity and the calculation task size in the user calculation task data; the preset unloading decision constraints, the target task completion delay, the target task completion energy consumption, the preset UAV constraints, the user UAV unloading data and the UAV coordinate data are used to construct a joint optimization problem; the joint optimization problem is transformed to determine the multi-agent Markov decision process model; finally, a multi-agent deep reinforcement learning algorithm is used to optimize and solve the multi-agent Markov decision process model to generate a target UAV task calculation flight strategy; based on the above scheme, the acquired user calculation task data, user coordinate data, and UAV coordinate data are processed to obtain the target task completion delay and target task completion energy consumption, and then combined with the preset unloading decision constraints, preset UAV constraints, user UAV unloading data, and UAV status data to obtain a multi-agent Markov decision process model, and the model is solved by a multi-agent deep reinforcement learning algorithm to generate a process of calculating the target UAV task flight strategy. The present invention comprehensively considers the delay and energy consumption, so that the same order of magnitude of changes are produced in the optimization process. At the same time, the present invention controls the UAV to calculate the user task according to the obtained target UAV task calculation flight strategy, and does not require the user end to unload and calculate at the same time, which can reduce energy consumption and thus reduce the processing cost of the computing task. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A flowchart of the steps of a method for optimizing a UAV mission flight strategy calculation provided in the first embodiment of the present invention;

[0046] Figure 2 A schematic diagram of the network architecture of the system model corresponding to the method for optimizing the flight strategy for calculating UAV missions provided in the first embodiment of the present invention;

[0047] Figure 3 This is a structural block diagram of a UAV mission calculation flight strategy optimization system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0048] The embodiments of the present invention provide a method and system for optimizing the flight strategy of a UAV mission calculation, which are used to solve the technical problem that the processing cost of computing tasks is too high due to the existing UAV mission calculation flight strategy optimization technology.

[0049] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 making creative work are within the scope of protection of the present invention.

[0050] See also Figure 1 , Figure 1 This is a flowchart of the steps of a method for optimizing a UAV mission calculation flight strategy provided in Example 1 of the present invention.

[0051] The present invention provides a method for optimizing a UAV mission calculation flight strategy, comprising:

[0052] Step 101: Obtain user computing task data, user coordinate data, drone status data, and drone coordinate data.

[0053] User computing task data includes computing task complexity, computing task size, and maximum tolerable delay time.

[0054] The drone coordinate data includes three-dimensional cluster drone coordinates, three-dimensional calculated drone coordinates, two-dimensional cluster drone coordinates, and two-dimensional calculated drone coordinates.

[0055] The user coordinate data includes three-dimensional user coordinates and two-dimensional user coordinates.

[0056] Please note that Figure 2 The system model corresponding to the UAV task calculation flight strategy optimization method proposed in the present invention is a three-layer network architecture of multi-UAV collaborative assisted mobile edge computing composed of ground terminal users U, cluster UAVs M and computing UAVs N, wherein ground terminal users U={1,2,...,u}, cluster UAVs M={1,2,...,m}, computing UAVs N={1,2,...,n}, n=1, that is, the present invention only considers one computing UAV (N=1), and ground users can offload tasks to cluster UAVs or computing UAVs (considering ground users not within the coverage range of the cluster UAVs to offload to computing UAVs). Among them, if the computing resources of the cluster UAV are limited and cannot meet its computing needs, the cluster UAV can offload to other cluster UAVs or computing UAVs for computing, and the cluster UAVs can process tasks in parallel.

[0057] Furthermore, the present invention divides time into multiple time slots t, and uses T={1,2,...,t} to represent the time slot set. Each user in each time slot t will generate corresponding user computing task data. The user computing task data generated by each ground user in the tth time slot is represented by a triple ,in, The size of the computing task generated by the u-th user (ground user) in the t-th time slot. The computing task can be offloaded to the cluster drone or computing drone for execution; The computational complexity of the task generated for the u-th user (ground user) in the t-th time slot, i.e., the number of CPU cycles required to compute 1 bit of input data, usually depends on the type of application; is the maximum tolerable delay time, that is, the maximum time to complete the computing task in the tth time slot. It is meaningless to complete the computing task beyond this time.

[0058] Furthermore, in order to reduce the impact of the change in flight altitude on the energy consumption of the swarm UAVs, the swarm UAVs m are set to a fixed altitude h m The three-dimensional coordinates of the flight, that is, the swarm drones (three-dimensional swarm drone coordinates) can be expressed as: , the two-dimensional coordinates of the cluster UAV can be expressed as: By setting the fifth and sixth drone constraints, the horizontal flight angle of the cluster drones is limited to , the flight distance is ,in, is the three-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal coordinate of the mth swarm UAV at time slot t, is the vertical coordinate of the mth swarm drone at time slot t, is the altitude data of the mth swarm drone, is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal flight angle of the mth swarm UAV at time slot t, is the flight distance of the mth swarm drone at time slot t, The maximum flight distance allowed for swarm drones.

[0059] Based on the above foundation, the position update calculation formula of the cluster drone can be obtained, specifically:

[0060]

[0061] in, is the horizontal coordinate of the mth swarm UAV at time slot t+1; is the vertical coordinate of the mth swarm UAV at time slot t+1; is the horizontal coordinate of the mth swarm UAV at time slot t; is the vertical coordinate of the mth swarm UAV at time slot t; is the horizontal flight angle of the mth swarm UAV at time slot t; is the flight distance of the mth swarm UAV at time slot t.

[0062] Furthermore, in order to ensure that the swarm drone m moves within the corresponding service range, the two-dimensional swarm drone coordinates of the swarm drone m need to satisfy the set first drone constraint: ,in, is the horizontal coordinate of the mth swarm UAV at time slot t, is the vertical coordinate of the mth swarm drone at time slot t, The maximum length of the service area divided by the mth cluster drone, The maximum width of the service area divided for the mth cluster of drones.

[0063] Furthermore, the three-dimensional user coordinates are , the two-dimensional user coordinates are Based on the set second UAV constraint, when the ground user wants to offload the task to the cluster UAV m, he needs to be within the horizontal coverage of the cluster UAV m. The second UAV constraint is specifically: ,in, is the two-dimensional user coordinate of the u-th user at time slot t, is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal coverage of the mth swarm drone.

[0064] Furthermore, in order to avoid collision between any two swarm drones and to avoid overlapping coverage, the distance between swarm drones should be no less than the minimum safety distance d according to the set third drone constraint and fourth drone constraint. min , the coverage between cluster drones should be no less than twice the horizontal coverage of the cluster drones, where the third drone constraint can be expressed as: ; The fourth UAV constraint can be expressed as: , is the three-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional coordinates of a cluster UAV at time slot t, is the minimum safe distance, A collection of swarm drones. is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The two-dimensional coordinates of a swarm UAV at time slot t.

[0065] Step 102: Determine the user-drone distance data based on the user coordinate data and the drone coordinate data, and determine the task transmission offloading rate based on the user-drone distance data and the drone height data in the drone coordinate data.

[0066] The user drone distance data includes the distance data between the user and the cluster drone, the distance data between the user and the calculation drone, and the distance data between drones; among them, the distance data between drones includes the distance between cluster drones (that is, the distance between a cluster drone m and another cluster drone m'), and the distance between a cluster drone and the calculation drone.

[0067] The task transmission offloading rate includes the transmission rate between users and drones and the task offloading rate between drones.

[0068] Specifically, the process of determining the user-drone distance data based on the user coordinate data and the drone coordinate data can be achieved by executing the following steps S11 to S12:

[0069] Step S11: perform Euclidean distance calculations on the three-dimensional user coordinates in the user coordinate data, the three-dimensional cluster drone coordinates, and the three-dimensional calculation drone coordinates, and output the distance data between the user and the cluster drone and the distance data between the user and the calculation drone;

[0070] Step S12: Determine the distance data between the drones based on the three-dimensional cluster drone coordinates and the three-dimensional calculated drone coordinates.

[0071] It should be noted that, similar to existing research on drone edge computing, the system model proposed in this paper is based on a quasi-static scenario, that is, during data transmission, the network topology of the swarm drones and ground users remains fixed. In the Cartesian coordinate system, the Euclidean distance calculation formula can be used to calculate the distance between ground user u and swarm drone m or computing drone n, and the distance between swarm drone m and swarm drone m' or computing drone n at time slot t. This process can be expressed as:

[0072]

[0073] in, is the distance between the u-th user and the m-th cluster drone at time slot t; is the three-dimensional user coordinate of the u-th user at time slot t; is the three-dimensional cluster UAV coordinate of the mth cluster UAV at time slot t; Calculate the distance between the u-th user and the n-th drone at time slot t; The three-dimensional coordinates of the n-th drone are calculated. The position of the drone is fixed, that is, the three-dimensional coordinates of the drone remain unchanged. , is the horizontal coordinate of the nth calculated drone, Calculate the vertical coordinate of the n-th drone, Calculate the drone altitude data for the nth drone; is the distance between the mth swarm drone and the m'th swarm drone at time slot t; is the three-dimensional cluster UAV coordinate of the m'th cluster UAV at time slot t; is the distance between the mth cluster UAV and the nth calculation UAV at time slot t; M is the set of cluster UAVs.

[0074] Furthermore, the process of determining the task transmission offloading rate based on the user drone distance data and the drone altitude data in the drone coordinate data can be achieved by executing the following steps S21 to S24:

[0075] Step S21: Determine the connection probability between the user and the drone based on the distance data between the user and the cluster drone, the distance data between the user and the calculation drone, and the drone height data in the drone coordinate data;

[0076] The user-UAV connection probability includes the LoS connection probability between the ground user u and the swarm UAV m or the computing UAV n at time slot t, and the NLoS connection probability between the ground user u and the swarm UAV m or the computing UAV n at time slot t.

[0077] It should be noted that since the communication channel between the ground user u and the swarm UAV m or computing UAV n is affected by the altitude, elevation angle, and propagation environment type, its communication link model is established as a probabilistic path loss model that includes LoS (Line of Sight) and NLoS (Non Line of Sight) with different occurrence probabilities. Therefore, the calculation process of the LoS connection probability between the ground user u and the swarm UAV m or computing UAV n in time slot t can be expressed as:

[0078]

[0079] in, Calculate the LoS connection probability between the u-th ground user and the m-th swarm UAV or the n-th UAV at time slot t; is the first environmental parameter (e.g. rural or urban); is the second environmental parameter; It is the altitude data (drone altitude data in drone coordinate data), including the drone altitude data of the calculation drone and the drone altitude data of the cluster drone. , is the altitude data of the mth swarm drone, Calculate the drone altitude data for the nth drone; For distance data (user drone distance data), including, , is the distance between the u-th user and the m-th cluster drone at time slot t, Calculate the distance between the u-th user and the n-th drone at time slot t.

[0080] Furthermore, according to the LoS connection probability between the ground user u and the swarm UAV m or computing UAV n in time slot t, the NLoS connection probability between the ground user u and the swarm UAV m or computing UAV n in time slot t is determined. This process can be expressed as:

[0081]

[0082] in, Calculate the NLoS connection probability for the u-th ground user and the m-th swarm UAV or the n-th UAV at time slot t; Calculate the LoS connection probability between the u-th ground user and the m-th swarm UAV or the n-th UAV at time slot t.

[0083] Step S22: Determine the channel power gain of the link between the user and the drone based on the connection probability between the user and the drone, the distance data between the user and the cluster drone, and the distance data between the user and the computing drone.

[0084] The link channel power gain between user and UAV is the link channel power gain from user u to cluster UAV m or to computing UAV n.

[0085] Specifically, based on the distance data between the user and the cluster drone and the distance data between the user and the computing drone, the LoS path loss from the ground user u to the cluster drone m or computing drone n, the NLoS path loss from the ground user u to the cluster drone m or computing drone n, and the free space path consumption are determined. The calculation process can be expressed as:

[0086]

[0087] in, Calculate the LoS path loss for the u-th ground user and the m-th swarm drone or the n-th drone; is the free space path consumption; is the additional loss of LoS; Calculate the NLoS path loss for the u-th ground user and the m-th swarm UAV or the n-th UAV; is the additional loss of NLoS; f is the carrier frequency; D is the distance data (user drone distance data), including, , is the distance between the u-th user and the m-th cluster drone at time slot t, The distance between the u-th user and the n-th drone is calculated at time slot t; c is the speed of light.

[0088] Furthermore, based on the free space path consumption obtained in the above steps, the LoS connection probability between the ground user u and the clustered UAV m or the calculated UAV n in time slot t, the LoS path loss between the ground user u and the clustered UAV m or the calculated UAV n, the NLoS connection probability between the ground user u and the clustered UAV m or the calculated UAV n in time slot t, and the NLoS path loss between the ground user u and the clustered UAV m or the calculated UAV n, the power gain of the user-UAV link channel is determined. This process can be expressed as:

[0089]

[0090] wherein, is the path loss of the LoS between the u-th ground user and the m-th swarm drone or the n-th computing drone; is the free space path loss; is the LoS connection probability between the u-th ground user and the m-th swarm drone or the n-th computing drone at time slot t; is the NLoS connection probability between the u-th ground user and the m-th swarm drone or the n-th computing drone at time slot t; is the path loss of the NLoS between the u-th ground user and the m-th swarm drone or the n-th computing drone; is the link channel power gain of the u-th user to the m-th swarm drone or to the n-th computing drone at time slot t.

[0091] Step S23, determining the user-to-drone transmission rate according to the user-to-drone link channel power gain by using the preset Shannon formula;

[0092] The user-to-drone transmission rate is the transmission rate of the task offloading of the ground user u to the swarm drone m or to the computing drone n.

[0093] It should be noted that the process of determining the user-to-drone transmission rate according to the user-to-drone link channel power gain by using the preset Shannon formula can be expressed as:

[0094]

[0095] wherein, is the transmission rate of the task offloading of the u-th ground user to the m-th swarm drone or to the n-th computing drone at time slot t; is the channel bandwidth between the u-th user and the m-th swarm drone or the n-th computing drone; is the transmit power of the u-th user; is the link channel power gain of the u-th user to the m-th swarm drone or to the n-th computing drone; is the additive white Gaussian noise power.

[0096] Step S24, determining the inter-drone task offloading rate based on the inter-drone distance data.

[0097] The inter-drone task offloading rate is the task offloading rate of the swarm drone m to the swarm drone m’ or to the computing drone n.

[0098] It should be noted that since the swarm drones and the computing drone are at a relatively high altitude, the communication between them is considered to be LoS (Line of Sight) communication. Specifically, based on the distance data between drones, that is, the distance between swarm drones (that is, the distance between swarm drone m and another swarm drone m') or the distance between a swarm drone and a computing drone, the channel power gain between swarm drone m and swarm drone m' or computing drone n is determined. This process can be expressed as:

[0099]

[0100] in, Calculate the channel power gain of the mth cluster UAV and the m'th cluster UAV or the nth UAV at time slot t; is the distance between the mth cluster UAV and the m'th cluster UAV or the nth calculation UAV at time slot t; is the channel power gain at a reference distance of 1 m; M is the set of clustered drones; N is the set of computing drones.

[0101] Furthermore, the task offloading rate between drones is determined based on the channel power gain between the cluster drone m and the cluster drone m' or the calculated drone n. This process can be expressed as:

[0102]

[0103] in, is the task offloading rate of the mth cluster UAV and the m'th cluster UAV or the nth computing UAV at time slot t; is the channel bandwidth between the mth cluster UAV and the nth computing UAV or the m'th cluster UAV; Calculate the channel power gain of the mth cluster UAV and the m'th cluster UAV or the nth UAV at time slot t; is the transmission power of the mth swarm UAV; is the additive white Gaussian noise power.

[0104] Step 103: Based on the preset offloading decision constraints, determine the user drone offloading data, and determine the target task completion delay and target task completion energy consumption according to the user drone offloading data, drone status data, task transmission offloading rate, and computing task complexity and computing task size in the user computing task data.

[0105] The user drone unloading data includes first unloading data, second unloading data, third unloading data and fourth unloading data.

[0106] The computational task complexity includes the computational task complexity of a single UAV and the computational task complexity of a cluster of UAVs.

[0107] The computing task size includes the computing task size of the UAV and the computing task size of the swarm UAV.

[0108] The user-drone transmission rate includes the user-computing drone transmission rate and the user-swarm drone transmission rate.

[0109] It should be noted that the ground user u generates a computing task in time slot t , considering that the ground user has no local computing power, user u can only offload tasks to the cluster drone m or computing drone n. In order to avoid interference, at any time, the user can only offload the tasks it generates to one drone (including cluster drones and computing drones). Its offloading decision (the first offloading decision constraint) can be expressed as:

[0110]

[0111] in, The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the u-th user offloads the task to the n-th computing drone for calculation at time slot t; U is the set of time slots; M is the set of cluster drones; N is the set of computing drones.

[0112] Furthermore, after receiving the task offloaded by the user, the swarm drone can choose to perform local computing or offload to other swarm drones or computing drones. Its offloading decision (second offloading decision constraint, third offloading decision constraint, fourth offloading decision constraint, and fifth offloading decision constraint) is described as follows:

[0113]

[0114] in, The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When , the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t; The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When , the mth cluster drone performs local calculation on the task at time slot t; The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When t is t, the mth swarm drone performs local computation on the task at time slot t, where M is the set of swarm drones.

[0115] It is worth mentioning that the present invention takes into account the flight status of the swarm UAV m (i.e., whether it is faulty), that is, the UAV status data of each swarm UAV is obtained. The UAV status data can be expressed as: , among which, when When , it means that the mth cluster drone is in a fault state at time slot t. , it means that the mth cluster drone is in a normal state (no fault) at time slot t.

[0116] Furthermore, the process of determining the target task completion delay and the target task completion energy consumption based on the user drone unloading data, drone status data, task transmission unloading rate, and the computing task complexity and computing task size in the user computing task data can be achieved by executing the following steps S31 to S34:

[0117] Step S31, determining the completion delay of the user unloading the computing drone task and the energy consumption of the user unloading the computing drone task based on the computing drone computing task complexity, the computing drone computing task size, and the transmission rate between the user and the computing drone;

[0118] The completion delay of the user offloading computing UAV task is the task completion delay of the ground user offloading to the computing UAV in time slot t.

[0119] The energy consumption of user offloading computing UAV task completion is the computing energy consumption of the ground user offloading the computing UAV task completion in time slot t.

[0120] It should be noted that when the ground user is not within the coverage of the swarm UAV, the ground user can offload the tasks it generates to the computing UAV. That is, based on the computing task complexity of the computing UAV, the computing task size of the computing UAV, and the transmission rate between the user and the computing UAV, the task completion delay of the ground user offloaded to the computing UAV in time slot t and the computing energy consumption of the task completed by the ground user offloaded to the computing UAV in time slot t can be determined. This process can be expressed as:

[0121]

[0122] in, is the task completion delay of the u-th ground user offloading to the n-th computing UAV at time slot t; The computational UAV task size offloaded from the u-th ground user to the n-th computational UAV at time slot t; Calculate the transmission rate of the UAV for offloading the task of the u-th ground user to the n-th one at time slot t; The computational complexity of the computational UAV task offloaded from the u-th ground user to the n-th computational UAV at time slot t; To calculate the CPU cycle frequency of the drone; is the computational energy consumption of the u-th ground user offloaded to the n-th computing UAV at time slot t; is the transmission power of the u-th ground user; The effective capacitance for calculating the UAV depends on the chip architecture.

[0123] Step S32: Determine the completion delay of the user unloading swarm drone task and the energy consumption of the user unloading swarm drone task based on the user drone unloading data, the complexity of the swarm drone computing task, the size of the swarm drone computing task, and the transmission rate between the user and the swarm drone;

[0124] The completion delay of the task that the user unloads from the swarm drone is the completion delay of the task that the user unloads from the swarm drone.

[0125] The energy consumption of completing tasks unloaded by users to cluster drones is the energy consumption of completing tasks unloaded by users to cluster drones.

[0126] It should be noted that in time slot t, when ground user u offloads to cluster drone m, cluster drone m can perform local computation or offload to other cluster drones m' or computing drone n for auxiliary computation. Based on the user drone offloading data, the complexity of the cluster drone computing task, the size of the cluster drone computing task, and the transmission rate between the user and the cluster drone, the completion delay of the user offloading cluster drone task and the energy consumption of the user offloading cluster drone task can be determined. This process can be expressed as:

[0127]

[0128]

[0129] in, is the task completion delay of the u-th user offloading to the m-th cluster drone at time slot t; The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When , the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t; is the time delay for the mth swarm UAV to complete the calculation locally at time slot t; The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When , the mth cluster drone performs local calculation on the task at time slot t; The mth cluster drone is unloaded to the The mission completion delay of a swarm of drones; The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When , the mth cluster drone performs local calculation on the task at time slot t; is the task completion delay of the mth cluster UAV unloading to the nth computing UAV at time slot t; Calculate the task size for the swarm UAV that offloads from the u-th ground user to the m-th swarm UAV at time slot t; is the transmission rate of the task offloading from the u-th ground user to the m-th cluster UAV at time slot t; Calculate the task complexity of the swarm UAVs for offloading from the u-th ground user to the m-th swarm UAV at time slot t; is the CPU cycle frequency of the mth cluster drone; To calculate the CPU cycle frequency of the drone; The energy consumption of the task completed by the u-th user offloading to the m-th cluster drone at time slot t; is the energy consumption calculated locally by the mth swarm UAV at time slot t; The mth cluster drone is unloaded to the Energy consumption of a swarm of drones to complete their mission; is the energy consumption of the task completed by the m-th cluster UAV offloaded to the n-th computing UAV at time slot t; is the transmission power of the u-th user; is the effective capacitance of the chip architecture of the mth swarm drone; is the transmission power of the mth swarm UAV; is the task offloading rate of the mth cluster UAV and the m'th cluster UAV at time slot t; is the CPU cycle frequency of the m'th cluster drone; Calculate the transmission rate of the UAV for offloading the task of the u-th ground user to the n-th one at time slot t; is the effective capacitance of the chip architecture of the m'th swarm drone; Calculate the effective capacitance of the chip architecture of the drone for the nth time.

[0130] Step S33: Determine the target task completion delay based on the drone status data, the user drone unloading data, the user unloading calculation drone task completion delay, and the user unloading cluster drone task completion delay;

[0131] The target task completion delay is the task completion delay of user u in time slot t.

[0132] It should be noted that based on the above derivation process, the target task completion delay is determined according to the drone status data, user drone unloading data, user unloading calculation drone task completion delay, and fixed user unloading cluster drone task completion delay. This process can be expressed as:

[0133]

[0134] in, The target task completion delay; The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the u-th user offloads the task to the n-th computing drone for calculation at time slot t; is the UAV status data of the mth cluster UAV at time slot t, when When , it means that the mth cluster drone is in a fault state at time slot t. When , it means that the mth swarm drone is in a normal state (no fault) at time slot t; is the task completion delay of the u-th user offloading to the m-th cluster drone at time slot t; is the task completion delay of the u-th ground user offloading to the n-th computing UAV at time slot t.

[0135] Step S34: Determine the target task completion energy consumption based on the drone status data, the user drone unloading data, the user unloading calculated drone task completion energy consumption, and the user unloading cluster drone task completion energy consumption.

[0136] The target task completion energy consumption is the energy consumption of the system task completed in time slot t.

[0137] It should be noted that based on the above derivation process, the target task completion energy consumption is determined according to the drone status data, user drone unloading data, user unloading calculation drone task completion energy consumption, and user unloading cluster drone task completion energy consumption. The processing process can be expressed as:

[0138]

[0139] in, Energy consumption for target task completion; The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the u-th user offloads the task to the n-th computing drone for calculation at time slot t; is the UAV status data of the mth cluster UAV at time slot t, when When , it means that the mth cluster drone is in a fault state at time slot t. When , it means that the mth swarm drone is in a normal state (no fault) at time slot t; The energy consumption of the task completed by the u-th user offloading to the m-th cluster drone at time slot t; is the computing energy consumption offloaded from the u-th ground user to the n-th computing UAV at time slot t.

[0140] Step 104: Construct a joint optimization problem using preset offloading decision constraints, target task completion delay, target task completion energy consumption, preset drone constraints, user drone offloading data, and drone coordinate data.

[0141] Furthermore, step 104 may include the following sub-steps S41-S42:

[0142] Step S41: Determine the task completion cost based on the target task completion delay and the target task completion energy consumption;

[0143] It should be noted that the cost of the system (task completion cost) in time slot t is determined based on the target task completion delay and target task completion energy consumption. This process can be expressed as:

[0144]

[0145] in, Cost of completing the task; is the energy consumption weight; Energy consumption for target task completion; is the delay weight, satisfying ; The target task completion delay.

[0146] Step S42: Construct a joint optimization problem using preset offloading decision constraints, task completion costs, preset drone constraints, user drone offloading data, and drone coordinate data.

[0147] The user drone unloading data includes first unloading data, second unloading data, third unloading data and fourth unloading data.

[0148] The preset offloading decision constraints include a first offloading decision constraint, a second offloading decision constraint, a third offloading decision constraint, a fourth offloading decision constraint, and a fifth offloading decision constraint.

[0149] The preset drone constraints include a first drone constraint, a second drone constraint, a third drone constraint, a fourth drone constraint, a fifth drone constraint, a sixth drone constraint, a seventh drone constraint, and an eighth drone constraint.

[0150] The drone coordinate data also includes two-dimensional cluster drone coordinates.

[0151] It should be noted that the optimization goal of this invention is to minimize system energy consumption and user task completion delay by combining the flight trajectory and task allocation of UAVs. Therefore, based on the above derivation process, according to the task completion cost, user UAV unloading data and UAV coordinate data, as well as the preset unloading decision constraints and preset UAV constraints, the constructed joint optimization problem can be described as follows:

[0152]

[0153] Where P is a joint optimization problem; is the two-dimensional cluster UAV coordinate, which represents the optimization variable of the cluster UAV trajectory. , is the horizontal coordinate of the mth swarm UAV at time slot t, is the vertical coordinate of the mth cluster UAV at time slot t; B is the user UAV unloading data, which represents the unloading decision variables of the user and cluster UAV, , The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the uth user offloads the task to the nth computing drone for calculation at time slot t. The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When t is t, the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t. The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When t is t, the mth cluster drone performs local calculation on the task at time slot t. The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When , the mth swarm drone performs local computation on the task at time slot t, where M is the set of swarm drones; is the task completion cost at time slot t; T is the time slot set; C1 is the first offloading decision constraint, indicating that the task of each ground user (user) can only be offloaded to a cluster drone or a computing drone, and U is the set of users; C2 is the second offloading decision constraint, indicating the constraint of the offloading decision variables calculated locally by the cluster drone; C3 is the third offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to the computing drone, and N is the set of computing drones; C4 is the fourth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to another cluster drone; C5 is the fifth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone; C6 is the first drone constraint, indicating the mobility constraint of the service range of each cluster drone. The maximum length of the service area divided by the mth cluster drone, is the maximum width of the service area divided by the mth swarm drone; C7 is the second drone constraint, which means that the ground user can offload the task to the coverage constraint of the swarm drone. is the two-dimensional user coordinate of the u-th user at time slot t, is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal coverage of the mth swarm UAV; C8 is the constraint of the third UAV, which means the constraint to avoid collision between swarm UAVs. is the three-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional coordinates of a cluster UAV at time slot t, is the minimum safety distance; C9 is the fourth drone constraint, which means the constraint to avoid overlapping coverage between cluster drones. is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional cluster UAV coordinates of the cluster UAV at time slot t; C10 is the fifth UAV constraint, which represents the constraint of the horizontal flight angle of the cluster UAV. is the horizontal flight angle of the mth swarm UAV at time slot t; C11 is the sixth UAV constraint, which represents the constraint on the horizontal flight distance of the swarm UAV. is the flight distance of the mth swarm drone at time slot t, is the maximum flight distance allowed by the swarm drone; C12 is the seventh drone constraint, indicating the state of the swarm drone. is the state data of the mth cluster drone at time slot t, when is the state data of the mth cluster drone at time slot t, when is the state data of the mth cluster drone at time slot t, when is the target task completion delay, is the maximum tolerable delay time.

[0154] Step 105, converting the joint optimization problem to determine a multi-agent Markov decision process model.

[0155] It should be noted that, due to the discrete variable B and the continuous variable and the non-convex objective function and constraints, the problem P is a mixed integer nonlinear programming (MINLP) problem. Traditional heuristic algorithms are difficult to guarantee to obtain high-quality feasible solutions in dynamic and highly uncertain environments. Therefore, the algorithm of multi-agent deep reinforcement learning can be used to solve it.

[0156] Further, Markov decision process (MDP) can effectively handle the uncertainty and dynamics in the decision-making process, providing an effective mathematical framework for real-time and optimal task offloading decisions for unmanned aerial vehicles in time-varying environments. The Markov decision process model is based on state, action, reward, and transition probability elements to describe how an agent achieves a goal by taking a series of actions within a given time frame. Therefore, in the multi-drone cluster assisted mobile edge computing, the cluster drones determine their positions and offloading decisions to obtain the minimum system task completion cost, considering that the movement of the unmanned aerial vehicles will affect the state of the environment, and the total cost of the system is determined by the current state of the system environment and the joint action of all unmanned aerial vehicles. Moreover, the previous state and the previous action jointly trigger the system environment to enter a new random state. In this case, the optimization problem P of task offloading can be expressed as a multi-agent Markov decision process model, which is specifically:

[0157]

[0158] where M is the set of agents, and the agent represents the cluster drone; S is the state set of all agents, i.e. the state space; A m is the action space of agent m; P is the state transition probability; R m is the reward function of agent m; is the discount factor, .

[0159] Furthermore, based on the optimization problem P, the state set S of each agent at time slot t is t The two-dimensional swarm drone coordinates of the swarm drone , drone status data of swarm drones , the user's two-dimensional user coordinates , the size of tasks generated by users The composition can be described as:

[0160]

[0161] Furthermore, the action of each swarm drone (agent) includes flight trajectory (horizontal flight distance and angle) and unloading decision, so the action space of the swarm drone can be expressed as:

[0162]

[0163] in, is the action of the mth swarm drone at time slot t; is the flight distance of the mth swarm UAV at time slot t; is the horizontal flight angle of the mth swarm UAV at time slot t; The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When , the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t; The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When , the mth cluster drone performs local calculation on the task at time slot t; The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When , the mth cluster drone performs local calculation on the task at time slot t; The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the u-th user offloads the task to the n-th computing drone for calculation at time slot t.

[0164] It is worth mentioning that according to the constraints of the optimization problem P, we can get The value range of each element in is: , , , , , .

[0165] Further, the M agents minimize the total cost of system task completion in a cooperative manner while satisfying relevant constraints, such as the constraints of coverage overlap and collision between UAVs. When all the constraints are satisfied, the reward function of the cluster UAV m is defined as the negative of the system cost C t ; when some of the constraints are not satisfied, the reward function will be punished accordingly. The reward function of the agent m at the time slot t is represented as:

[0166]

[0167] wherein, is the reward value at the time slot t; is the task completion cost; is the penalty corresponding to the overlap constraint; is the penalty corresponding to the collision constraint; is the penalty corresponding to the coverage constraint.

[0168] Further, in a dynamic time-varying environment, the transition probability plays a key role in the selection of the unloading decision of the UAV in the current state. The transition probability can be defined as P(S t+1 |S t ,A t ), wherein S t is the current state of the agent, A t is the action currently selected by the agent, and S t+1 is the state of the agent at the time slot t+1.

[0169] Step 106, a multi-agent deep reinforcement learning algorithm is used to optimize and solve the multi-agent Markov decision process model, and a target UAV task calculation flight strategy is generated.

[0170] It should be noted that based on the Markov decision process model established above, the multi-agent deep reinforcement learning algorithm can be used to solve the optimal solution, thereby obtaining the target drone task calculation flight strategy, and controlling the drone to calculate the task according to the target drone task calculation flight strategy, so as to obtain the optimal task calculation result, minimize the system energy consumption and minimize the user task completion delay, thereby reducing the processing cost of the calculation task. At the same time, the present invention comprehensively considers the delay and energy consumption, and introduces a parameter between the delay and energy consumption to adjust the two different dimensions, so that the same order of magnitude changes are produced in the optimization process, and will not be ignored because the order of magnitude is too small; the present invention also considers designing layered drones, first dividing the service area for the cluster drones, if the ground user is within the coverage range of the cluster drone, the task can be offloaded to the cluster drone, when the cluster drone computing resources are insufficient, the task component will be offloaded again to the same-layer drone or the upper-layer drone server for calculation through the server. This can greatly reduce the delay and energy consumption caused by wireless communication; in addition, the present invention considers that in the scenario of multiple drones assisting mobile edge computing, when a drone fails, other drones can replan the path and re-divide the service area, and cover the user equipment originally covered by the faulty drone within the coverage range of the normal drone to provide computing services for it and complete the remaining computing tasks, thereby ensuring the user's service quality, and achieving the optimal overall task delay and system energy consumption by optimizing drone trajectories and offloading decisions.

[0171] In this embodiment, the present invention is based on a three-layer network architecture computing offloading model of ground users, cluster drones and drones. In the multi-drone collaborative assisted mobile edge computing offloading scenario, the status of drones in the drone cluster (whether it is faulty) is taken into account, and after considering the drone failure, the service area is redistributed by adjusting the drone trajectory to ensure the service quality of users in the area covered by the faulty drone; at the same time, the present invention combines the flight trajectory and task allocation of the drone to minimize system energy consumption and user task completion delay.

[0172] For a comparison of technical performance, existing technologies can be used as a reference. In recent years, drone technology has experienced rapid development. Due to its advantages, such as small size, maneuverability, ease of deployment, high adaptability, and high probability of line-of-sight links, it has been widely used not only in the military but also in civilian applications. Drone applications have made significant progress in disaster relief, communication assistance, and network coverage. Drones can hover and fly. As high-altitude mobile platforms, they can carry mobile edge computing servers, addressing the challenges of traditional mobile edge computing deployments and providing more flexible and efficient computing and communication services. Drones, particularly in remote areas and disaster zones, can be quickly deployed as MEC nodes, ensuring continuous network connectivity and data processing, significantly improving emergency response capabilities and network service quality.

[0173] With the development of autonomous drone technology, multi-UAV collaborative control has become an important research direction. Through collaborative communication, multiple UAVs demonstrate high mission execution efficiency, excellent coordination, intelligence, and autonomy. To ensure the collaborative control performance of multi-UAV systems during mission execution, the flight status of UAVs has become a research focus. When multiple UAVs collaborate to perform tasks such as environmental monitoring, fire detection, and collaborative search, there is a risk of loss of control if one or more UAVs experience component failure. In severe cases, the faulty UAV may collide with surrounding UAVs, causing the entire flight formation to lose control. Therefore, research on coping with UAV failures and fault-tolerant collaborative control is of great theoretical significance and practical necessity for the safe execution of monitoring missions and the safe control of multi-UAV systems.

[0174] It's worth noting that actuators and sensors can wear out and age over extended operating time and in increasingly complex and severe environments. Furthermore, in multi-UAV cooperative formation flight, the number of system components increases significantly. Multi-UAV systems involve communication network connections, which can create opportunities for faulty UAVs to transmit erroneous status information to nearby UAVs, significantly increasing the probability of collision and mission failure.

[0175] In summary, existing drone mission calculation flight strategy optimization technologies consider splitting tasks between local user computation and drone computation. However, they fail to account for the limited computing resources of drones, resulting in the inability to process tasks offloaded by users. Furthermore, they only optimize energy consumption, not latency. Furthermore, existing drone mission calculation flight strategy optimization technologies assume that a task can be computed locally or offloaded to a drone for computation, leaving the user to decide how to offload subtasks. However, simultaneous offloading and computation by the user consumes significant energy. Furthermore, existing drone mission calculation flight strategy optimization technologies fail to account for drone failures, failing to guarantee that user-offloaded tasks will be covered by the faulty drone's computation. Furthermore, they fail to consider how tasks handled by users within the drone's coverage area should a drone fail.

[0176] To address the above issues, the present invention proposes a method for optimizing drone mission computation flight strategies. In a multi-drone-assisted mobile edge computing scenario, when a drone fails, other drones can replan their routes and redivide their service areas, bringing user devices covered by the original failed drone back into their own coverage areas to provide computing services and complete the remaining computing tasks, thereby ensuring user service quality and minimizing task completion latency and system energy consumption. Specifically, the present invention comprehensively considers latency and energy consumption, introducing a parameter between latency and energy consumption to adjust the two different dimensions, thereby ensuring that changes of the same order of magnitude occur during the optimization process, rather than being ignored due to small orders of magnitude. Furthermore, the present invention considers designing hierarchical drones, first dividing the service areas for swarm drones. If a ground user is within the coverage area of ​​a swarm drone, the task can be offloaded to the swarm drone. When the swarm drone's computing resources are insufficient, the server will offload the task components again to the drones on the same layer or the drone server on the upper layer for computation. This can greatly reduce the delay and energy consumption caused by wireless communication; in addition, the present invention considers that in the scenario of multiple drones assisting mobile edge computing, when a drone fails, other drones can replan the path and re-divide the service area, and cover the user equipment originally covered by the faulty drone within the coverage range of the normal drone to provide computing services for it and complete the remaining computing tasks, thereby ensuring the user's service quality, and achieving the optimal overall task delay and system energy consumption by optimizing drone trajectories and offloading decisions.

[0177] In an embodiment of the present invention, the present invention provides a method for optimizing a UAV task calculation flight strategy. First, user calculation task data, user coordinate data, UAV status data, and UAV coordinate data are obtained; then, based on the user coordinate data and the UAV coordinate data, the user UAV distance data is determined, and the task transmission unloading rate is determined according to the user UAV distance data and the UAV height data in the UAV coordinate data; based on a preset unloading decision constraint, the user UAV unloading data is determined, and according to the user UAV unloading data, the UAV status data, the task transmission unloading rate, and the calculation task complexity and the calculation task size in the user calculation task data, the target task completion delay and the target task completion energy consumption are determined; the preset unloading decision constraint, the target task completion delay, the target task completion energy consumption, the preset UAV constraint, the user UAV unloading data, and the UAV coordinate data are used to construct a joint optimization problem; the joint optimization problem is transformed to determine a multi-agent Markov decision. Strategy process model; finally, a multi-agent deep reinforcement learning algorithm is used to optimize and solve the multi-agent Markov decision process model to generate a target UAV task calculation flight strategy; based on the above scheme, the acquired user calculation task data, user coordinate data, and UAV coordinate data are processed to obtain the target task completion delay and target task completion energy consumption, and then combined with the preset unloading decision constraints, preset UAV constraints, user UAV unloading data, and UAV status data to obtain a multi-agent Markov decision process model, and the model is solved by a multi-agent deep reinforcement learning algorithm to generate a target UAV task calculation flight strategy process. The present invention comprehensively considers the delay and energy consumption, so that the same order of magnitude of changes are produced in the optimization process. At the same time, the present invention controls the UAV to calculate the user task according to the obtained target UAV task calculation flight strategy. There is no need for the user end to unload and calculate at the same time, which can reduce energy consumption and thus reduce the processing cost of the computing task.

[0178] See also Figure 3 , Figure 3 This is a structural block diagram of a UAV mission calculation flight strategy optimization system provided in Example 2 of the present invention.

[0179] The present invention provides a UAV mission calculation flight strategy optimization system, comprising:

[0180] Acquisition module 301, used to acquire user computing task data, user coordinate data, drone status data and drone coordinate data;

[0181] a determination module 302 for determining user-drone distance data based on the user coordinate data and the drone coordinate data, and determining a task transmission offloading rate based on the user-drone distance data and the drone altitude data in the drone coordinate data;

[0182] The delay and energy consumption determination module 303 is used to determine the user drone unloading data based on the preset unloading decision constraints, and determine the target task completion delay and target task completion energy consumption according to the user drone unloading data, drone status data, task transmission unloading rate, and the computing task complexity and computing task size in the user computing task data;

[0183] A construction module 304 is used to construct a joint optimization problem using preset offloading decision constraints, target task completion delay, target task completion energy consumption, preset UAV constraints, user UAV offloading data, and UAV coordinate data;

[0184] A conversion module 305 is used to convert the joint optimization problem and determine a multi-agent Markov decision process model;

[0185] The solution module 306 is used to optimize and solve the multi-agent Markov decision process model using a multi-agent deep reinforcement learning algorithm to generate a target UAV mission calculation flight strategy.

[0186] Furthermore, the drone coordinate data includes three-dimensional cluster drone coordinates and three-dimensional calculated drone coordinates; the user drone distance data includes distance data between the user and the cluster drone, distance data between the user and the calculated drone, and distance data between drones; the determination module 302 includes:

[0187] The first submodule is used to perform Euclidean distance calculations on the three-dimensional user coordinates in the user coordinate data, the three-dimensional cluster drone coordinates, and the three-dimensional calculation drone coordinates, and output the distance data between the user and the cluster drone and the distance data between the user and the calculation drone;

[0188] The second submodule is used to determine the distance data between drones based on the three-dimensional cluster drone coordinates and the three-dimensional calculated drone coordinates.

[0189] Furthermore, the task transmission offloading rate includes the transmission rate between the user and the drone and the task offloading rate between drones; the determination module 302 further includes:

[0190] The third submodule is used to determine the connection probability between the user and the drone based on the distance data between the user and the cluster drone, the distance data between the user and the computing drone, and the drone height data in the drone coordinate data;

[0191] The fourth submodule is used to determine the link channel power gain between the user and the drone based on the connection probability between the user and the drone, the distance data between the user and the cluster drone, and the distance data between the user and the computing drone;

[0192] The fifth submodule is used to determine the transmission rate between the user and the UAV based on the power gain of the link channel between the user and the UAV using a preset Shannon formula;

[0193] The sixth submodule is used to determine the task offloading rate between drones based on the distance data between drones.

[0194] Furthermore, the computational task complexity includes the computational task complexity of the computational drone and the computational task complexity of the swarm drone; the computational task size includes the computational task size of the computational drone and the computational task size of the swarm drone; the user-drone transmission rate includes the user-computing drone transmission rate and the user-swarm drone transmission rate; and the delay energy consumption determination module 303 is specifically configured to:

[0195] According to the computing task complexity of the computing drone, the computing task size of the computing drone, and the transmission rate between the user and the computing drone, the completion delay of the user offloading computing drone task and the energy consumption of the user offloading computing drone task are determined;

[0196] Based on the user drone unloading data, the complexity of the cluster drone computing task, the size of the cluster drone computing task, and the transmission rate between the user and the cluster drone, the completion delay of the user unloading cluster drone task and the energy consumption of the user unloading cluster drone task are determined;

[0197] Determine the target task completion delay based on drone status data, user drone unloading data, user unloading calculation drone task completion delay, and user unloading cluster drone task completion delay;

[0198] The target task completion energy consumption is determined based on drone status data, user drone unloading data, user unloading calculation drone task completion energy consumption, and user unloading cluster drone task completion energy consumption.

[0199] Furthermore, the construction module 304 is specifically configured to:

[0200] Determine the task completion cost based on the target task completion delay and target task completion energy consumption;

[0201] The pre-set offloading decision constraints, task completion cost, pre-set UAV constraints, user UAV offloading data and UAV coordinate data are used to construct a joint optimization problem.

[0202] Furthermore, the user drone unloading data includes first unloading data, second unloading data, third unloading data, and fourth unloading data; the preset unloading decision constraints include first unloading decision constraints, second unloading decision constraints, third unloading decision constraints, fourth unloading decision constraints, and fifth unloading decision constraints; the preset drone constraints include first drone constraints, second drone constraints, third drone constraints, fourth drone constraints, fifth drone constraints, sixth drone constraints, seventh drone constraints, and eighth drone constraints; the drone coordinate data also includes two-dimensional cluster drone coordinates; the joint optimization problem is specifically:

[0203]

[0204] Where P is a joint optimization problem; is the two-dimensional cluster UAV coordinate, which represents the optimization variable of the cluster UAV trajectory. , is the horizontal coordinate of the mth swarm UAV at time slot t, is the vertical coordinate of the mth cluster UAV at time slot t; B is the user UAV unloading data, which represents the unloading decision variable of the user and cluster UAV, B= , The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the uth user offloads the task to the nth computing drone for calculation at time slot t. The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When t is t, the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t. The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When t is t, the mth cluster drone performs local calculation on the task at time slot t. The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When , the mth swarm drone performs local computation on the task at time slot t, where M is the set of swarm drones; is the task completion cost at time slot t; T is the time slot set; C1 is the first offloading decision constraint, indicating that the task of each ground user (user) can only be offloaded to a cluster drone or a computing drone, and U is the set of users; C2 is the second offloading decision constraint, indicating the constraint of the offloading decision variables calculated locally by the cluster drone; C3 is the third offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to the computing drone, and N is the set of computing drones; C4 is the fourth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to another cluster drone; C5 is the fifth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone; C6 is the first drone constraint, indicating the mobility constraint of the service range of each cluster drone. The maximum length of the service area divided by the mth cluster drone, is the maximum width of the service area divided by the mth swarm drone; C7 is the second drone constraint, which means that the ground user can offload the task to the coverage constraint of the swarm drone. is the two-dimensional user coordinate of the u-th user at time slot t, is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal coverage of the mth swarm UAV; C8 is the constraint of the third UAV, which means the constraint to avoid collision between swarm UAVs. is the three-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional coordinates of a cluster UAV at time slot t, is the minimum safety distance; C9 is the fourth drone constraint, which means the constraint to avoid overlapping coverage between cluster drones. is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional cluster UAV coordinates of the cluster UAV at time slot t; C10 is the fifth UAV constraint, which represents the constraint of the horizontal flight angle of the cluster UAV. is the horizontal flight angle of the mth swarm UAV at time slot t; C11 is the sixth UAV constraint, which represents the constraint on the horizontal flight distance of the swarm UAV. is the flight distance of the mth swarm drone at time slot t, is the maximum flight distance allowed by the swarm drone; C12 is the seventh drone constraint, indicating the state of the swarm drone. is the UAV status data of the mth cluster UAV at time slot t, when When , it means that the mth cluster drone is in a fault state at time slot t. When , it means that the mth cluster drone is in a normal state at time slot t; C13 is the eighth drone constraint, which means that the computing task in the tth time slot needs to be completed before the maximum tolerable delay. is the target task completion delay, is the maximum tolerable delay time.

[0205] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, modules and sub-modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0206] An embodiment of the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the drone mission calculation flight strategy optimization method as described in any of the above embodiments.

[0207] An embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method for optimizing the flight strategy for calculating a drone mission as described in any of the above embodiments are implemented.

[0208] An embodiment of the present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for optimizing the flight strategy for calculating a drone mission as described in any of the above embodiments.

[0209] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0210] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0211] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing flight strategy for UAV mission calculation, characterized in that: include: Obtaining user computing task data, user coordinate data, drone status data, and drone coordinate data; the drones include cluster drones and computing drones; Determining user-drone distance data based on the user coordinate data and the drone coordinate data, and determining a task transmission offloading rate based on the user-drone distance data and drone altitude data in the drone coordinate data; Based on the preset offloading decision constraints, determine the user drone offloading data, and determine the target task completion delay and target task completion energy consumption according to the user drone offloading data, the drone status data, the task transmission offloading rate, and the computing task complexity and computing task size in the user computing task data; A joint optimization problem is constructed using the preset offloading decision constraints, the target task completion delay, the target task completion energy consumption, preset drone constraints, the user drone offloading data, and the drone coordinate data; Transforming the joint optimization problem to determine a multi-agent Markov decision process model; A multi-agent deep reinforcement learning algorithm is used to optimize and solve the multi-agent Markov decision process model to generate a target UAV mission calculation flight strategy.

2. The method for optimizing the UAV mission calculation flight strategy according to claim 1, characterized in that: The drone coordinate data includes three-dimensional cluster drone coordinates and three-dimensional calculation drone coordinates; the user drone distance data includes distance data between the user and the cluster drone, distance data between the user and the calculation drone, and distance data between drones; The determining of user-drone distance data based on the user coordinate data and the drone coordinate data includes: Performing Euclidean distance calculations on the three-dimensional user coordinates in the user coordinate data, the three-dimensional cluster drone coordinates, and the three-dimensional calculation drone coordinates, and outputting distance data between the user and the cluster drone, and distance data between the user and the calculation drone; The distance data between the drones is determined based on the three-dimensional cluster drone coordinates and the three-dimensional calculated drone coordinates.

3. The method for optimizing the UAV mission calculation flight strategy according to claim 2, characterized in that: The task transmission offloading rate includes the transmission rate between the user and the drone and the task offloading rate between the drones; The determining of the task transmission offloading rate according to the user drone distance data and drone height data in the drone coordinate data includes: Determining a connection probability between the user and the drone based on the distance data between the user and the cluster drone, the distance data between the user and the calculation drone, and the drone height data in the drone coordinate data; Determine the link channel power gain between the user and the drone based on the connection probability between the user and the drone, the distance data between the user and the cluster drone, and the distance data between the user and the computing drone; The preset Shannon formula is used to determine the transmission rate between the user and the UAV according to the power gain of the link channel between the user and the UAV; Based on the distance data between the UAVs, a task offloading rate between the UAVs is determined.

4. The method for optimizing the UAV mission calculation flight strategy according to claim 3, characterized in that: The computing task complexity includes the computing task complexity of the UAV and the computing task complexity of the swarm UAV; the computing task size includes the computing task size of the UAV and the computing task size of the swarm UAV; The user-drone transmission rate includes the user-computing drone transmission rate and the user-swarm drone transmission rate; determining the target task completion delay and the target task completion energy consumption based on the user drone unloading data, the drone status data, the task transmission unloading rate, and the computing task complexity and computing task size in the user computing task data, including: According to the computing task complexity of the computing drone, the computing task size of the computing drone, and the transmission rate between the user and the computing drone, the completion delay of the user offloading computing drone task and the energy consumption of the user offloading computing drone task are determined; Determine the completion delay of the user unloading swarm drone task and the energy consumption of the user unloading swarm drone task based on the user drone unloading data, the complexity of the swarm drone computing task, the size of the swarm drone computing task, and the transmission rate between the user and the swarm drone; Determine the target task completion delay based on the drone status data, the user drone uninstallation data, the user uninstallation calculation drone task completion delay, and the user uninstallation cluster drone task completion delay; The target task completion energy consumption is determined based on the drone status data, the user drone unloading data, the user unloading calculation drone task completion energy consumption and the user unloading cluster drone task completion energy consumption.

5. The method for optimizing the UAV mission calculation flight strategy according to claim 1, characterized in that: The method constructs a joint optimization problem by using the preset unloading decision constraints, the target task completion delay, the target task completion energy consumption, the preset drone constraints, the user drone unloading data, and the drone coordinate data, including: Determining a task completion cost based on the target task completion delay and the target task completion energy consumption; A joint optimization problem is constructed using the preset offloading decision constraints, the task completion cost, the preset drone constraints, the user drone offloading data, and the drone coordinate data.

6. The method for optimizing the UAV mission calculation flight strategy according to claim 5, characterized in that: The user drone unloading data includes first unloading data, second unloading data, third unloading data, and fourth unloading data; the preset unloading decision constraints include first unloading decision constraints, second unloading decision constraints, third unloading decision constraints, fourth unloading decision constraints, and fifth unloading decision constraints; the preset drone constraints include first drone constraints, second drone constraints, third drone constraints, fourth drone constraints, fifth drone constraints, sixth drone constraints, seventh drone constraints, and eighth drone constraints; the drone coordinate data also includes two-dimensional cluster drone coordinates; the joint optimization problem is specifically: ； Where P is a joint optimization problem; is the two-dimensional cluster UAV coordinate, which represents the optimization variable of the cluster UAV trajectory. , is the horizontal coordinate of the mth swarm UAV at time slot t, is the vertical coordinate of the mth cluster UAV at time slot t; B is the user UAV unloading data, which represents the unloading decision variable of the user and cluster UAV, B= , The first uninstall data, indicating that When the uth user unloads the task to the mth cluster drone for calculation at time slot t, When , the uth user offloads the task to the nth computing drone for calculation at time slot t. The second uninstall data indicates that When , the mth cluster drone performs local calculations on the task at time slot t. When t is t, the mth cluster drone offloads the task to other drones for auxiliary calculation at time slot t. The third uninstall data indicates that When , the mth cluster drone unloads the task to the nth computing drone for auxiliary computing at time slot t. When t is t, the mth cluster drone performs local calculation on the task at time slot t. The fourth uninstall data indicates that When the mth cluster drone unloads the task to the mth drone at time slot t A cluster of drones performs auxiliary calculations. When , the mth swarm drone performs local computation on the task at time slot t, where M is the set of swarm drones; is the task completion cost at time slot t; T is the time slot set; C1 is the first offloading decision constraint, indicating that each ground user's task can only be offloaded to a cluster drone or a computing drone, and U is the set of users; C2 is the second offloading decision constraint, indicating the constraint of the offloading decision variables calculated locally by the cluster drone; C3 is the third offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to the computing drone, and N is the set of computing drones; C4 is the fourth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone offloading to another cluster drone; C5 is the fifth offloading decision constraint, indicating the constraint of the offloading decision variables of the cluster drone; C6 is the first drone constraint, indicating the mobility constraint of the service range of each cluster drone. The maximum length of the service area divided by the mth cluster drone, is the maximum width of the service area divided by the mth swarm drone; C7 is the second drone constraint, which means that the ground user can offload the task to the coverage constraint of the swarm drone. is the two-dimensional user coordinate of the u-th user at time slot t, is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, is the horizontal coverage of the mth swarm UAV; C8 is the constraint of the third UAV, which means the constraint to avoid collision between swarm UAVs. is the three-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional coordinates of a cluster UAV at time slot t, is the minimum safety distance; C9 is the fourth drone constraint, which means the constraint to avoid overlapping coverage between cluster drones. is the two-dimensional cluster drone coordinate of the mth cluster drone at time slot t, For the The three-dimensional cluster UAV coordinates of the cluster UAV at time slot t; C10 is the fifth UAV constraint, which represents the constraint of the horizontal flight angle of the cluster UAV. is the horizontal flight angle of the mth swarm UAV at time slot t; C11 is the sixth UAV constraint, which represents the constraint on the horizontal flight distance of the swarm UAV. is the flight distance of the mth swarm drone at time slot t, is the maximum flight distance allowed by the swarm drone; C12 is the seventh drone constraint, indicating the state of the swarm drone. is the UAV status data of the mth cluster UAV at time slot t, when When , it means that the mth cluster drone is in a fault state at time slot t. When , it means that the mth cluster drone is in a normal state at time slot t; C13 is the eighth drone constraint, which means that the computing task in the tth time slot needs to be completed before the maximum tolerable delay. is the target task completion delay, is the maximum tolerable delay time.

7. A UAV mission calculation flight strategy optimization system, characterized in that: include: An acquisition module is used to acquire user computing task data, user coordinate data, drone status data, and drone coordinate data; the drones include cluster drones and computing drones; a determination module, configured to determine user-drone distance data based on the user coordinate data and the drone coordinate data, and determine a task transmission offloading rate according to the user-drone distance data and drone altitude data in the drone coordinate data; a delay and energy consumption determination module, configured to determine user drone unloading data based on preset unloading decision constraints, and determine a target task completion delay and target task completion energy consumption based on the user drone unloading data, the drone status data, the task transmission unloading rate, and the computing task complexity and computing task size in the user computing task data; A construction module, configured to construct a joint optimization problem using the preset offloading decision constraints, the target task completion delay, the target task completion energy consumption, preset drone constraints, the user drone offloading data, and the drone coordinate data; A conversion module, configured to convert the joint optimization problem and determine a multi-agent Markov decision process model; The solution module is used to optimize and solve the multi-agent Markov decision process model using a multi-agent deep reinforcement learning algorithm to generate a target UAV mission calculation flight strategy.

8. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for optimizing the flight strategy of a drone mission calculation as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for optimizing the flight strategy for calculating a UAV mission as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the drone mission calculation flight strategy optimization method according to any one of claims 1 to 6.

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