Unmanned aerial vehicle communication deployment and scheduling method for adjusting tasks by facing to-be-adjusted unmanned aerial vehicle

Abstract

The invention provides an unmanned aerial vehicle communication deployment and scheduling method for an imminent adjustment task, and the method comprises the steps: constructing a discrete event system corresponding to each unmanned aerial vehicle based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space; under the discrete event system, taking maximization of the system utility of the unmanned aerial vehicle cluster under the current redeployment task as a target, and constructing an unmanned aerial vehicle redeployment optimization model under a preset constraint condition; solving the unmanned aerial vehicle re-deployment optimization model, obtaining an optimal re-deployment strategy of the unmanned aerial vehicle cluster under the current re-deployment task, and scheduling the unmanned aerial vehicles in the unmanned aerial vehicle cluster to execute the current re-deployment task based on the optimal re-deployment strategy; the system utility is obtained based on the total link capacity, the communication rate variance and the total energy consumption of the unmanned aerial vehicle cluster. According to the invention, the agility and flexibility of response to emergencies are improved, and the stability of communication services and the reliability of the unmanned aerial vehicle in execution of long-time tasks or complex and changeable scenes are improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for deploying and scheduling unmanned aerial vehicle communications for on-the-spot adjustment tasks. Background Art

[0002] UAVs have the advantages of low cost, high flexibility, strong independence and possible higher line of sight in wireless communication systems. Compared with traditional methods such as satellite communication and ground emergency communication vehicles, aerial base stations based on UAVs have the advantages of low cost, no terrain restrictions and no reliance on ground stations in emergency communication deployment. They can meet users' flexible communication needs, deploy in time and enhance communication capabilities and network robustness.

[0003] However, when facing actual application scenarios, existing UAV communication deployments have the problem of being unable to meet the system's rapid response to sudden dynamic demands. Although UAVs have shown many advantages in wireless communications, current deployment strategies often fail to fully consider the limited energy of UAVs in real environments and the time-varying nature of user needs, which limits the immediate adaptability and flexibility of UAV communication systems. In addition, current deployment strategies do not consider the sustainability of UAV systems, which affects the performance and reliability of UAVs in long-term missions or complex and changing scenarios.

[0004] Therefore, there is an urgent need to provide a UAV communication deployment and scheduling method for on-the-spot adjustment tasks to ensure the stability and continuity of UAV communication services. Summary of the invention

[0006] The present invention provides a UAV communication deployment and scheduling method for ad hoc adjustment tasks, so as to solve the defects of UAV communication deployment in the prior art.

[0007] The present invention provides a UAV communication deployment and scheduling method for ad hoc adjustment tasks, comprising the following steps: Based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, a discrete event system corresponding to each drone is constructed, wherein the event space includes at least one event that affects the state of the drone, and the state space includes the state of the drone after the event in the event space occurs; Under the discrete event system, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, a drone redeployment optimization model under preset constraints is constructed; Solving the drone redeployment optimization model to obtain the optimal redeployment strategy of the drone cluster under the current redeployment task, and scheduling the drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0008] According to a UAV communication deployment and scheduling method for ad hoc adjustment tasks provided by the present invention, the communication link capacity between the UAV and the user terminal served by the UAV is obtained in the following manner: Based on the straight-line distance between the UAV and the user terminal served by the UAV, the line-of-sight loss and non-line-of-sight loss between the UAV and the user terminal served by the UAV are obtained respectively; Based on the horizontal distance between the UAV and the user terminal served by the UAV and the flight altitude of the UAV, the probability of line-of-sight loss between the UAV and the user terminal served by the UAV is obtained; Based on the line-of-sight loss, the non-line-of-sight loss and the line-of-sight loss probability, obtaining an average path loss between the drone and a user terminal served by the drone; Based on the bandwidth allocated by the drone to the user terminal and the average path loss, the communication link capacity between the drone and the user terminal served by the drone is obtained.

[0009] According to a method for deploying and scheduling UAV communications for ad hoc adjustment tasks provided by the present invention, the method further includes: Based on the communication link capacity between the drones and the user terminals served by the drones, the total communication link capacity of the drone cluster is obtained; Based on the communication link capacity and the minimum target communication link capacity of the user terminal served by the drone, obtaining the communication rate variance of the drone cluster; Based on a first total energy consumption of a first drone and a second total energy consumption of a second drone in the drone cluster, the total energy consumption of the drone cluster is obtained.

[0010] According to a UAV communication deployment and scheduling method for ad hoc adjustment tasks provided by the present invention, the first total energy consumption of the first UAV and the second total energy consumption of the second UAV are obtained by the following method: Obtaining a first total energy consumption of the first drone based on a first duration corresponding to executing a redeployment algorithm operation after the first drone performs a power check, a second duration corresponding to the first drone moving from an original deployment position to a new deployment position, and the drone power; Based on a third time duration corresponding to the second UAV moving from the original deployment position to the new deployment position and the UAV power, a second total energy consumption of the second UAV is obtained.

[0011] According to a UAV communication deployment and scheduling method for ad hoc adjustment tasks provided by the present invention, the second duration corresponding to the first UAV moving from the original deployment position to the new deployment position and the third duration corresponding to the second UAV moving from the original deployment position to the new deployment position are obtained by the following method: Based on the horizontal flight speed of the first drone, the original deployment position of the first drone, and the new deployment position of the first drone, obtaining a second time duration corresponding to the first drone moving from the original deployment position to the new deployment position; Based on the horizontal flight speed of the second UAV, the original deployment position of the second UAV, the new deployment position of the second UAV, the vertical flight speed of the second UAV and the flight altitude of the second UAV, the third time duration corresponding to the second UAV moving from the original deployment position to the new deployment position is obtained.

[0012] According to a UAV communication deployment and scheduling method for ad hoc adjustment tasks provided by the present invention, the minimum energy consumption limit is obtained by the following method: Based on the horizontal flight speed of the UAV, the descent speed, the position of the UAV and the position of the central base station, the energy consumption of the first return flight of the UAV is obtained; Based on the first hovering energy consumption, the moving consumption and the first return energy consumption, the second return energy consumption of the drone is obtained, wherein the hovering energy consumption is the energy consumed by the drone when it performs a redeployment algorithm calculation after the power check, and the moving consumption is the energy consumed by the drone when it moves at a preset maximum moving distance; Based on the second return energy consumption and the second hovering energy consumption, the minimum energy consumption limit of the drone is obtained, where the second hovering energy consumption is the energy consumed when the drone hovers to serve the user terminal.

[0013] The present invention also provides a UAV communication deployment and scheduling device for ad hoc adjustment tasks, the device comprising: A first UAV communication deployment and scheduling module for on-the-spot adjustment tasks, configured to construct a discrete event system corresponding to each UAV based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, wherein the event space includes at least one event that affects the state of the UAV, and the state space includes the state of the UAV after the event in the event space occurs; The second UAV communication deployment and scheduling module for the ad hoc adjustment task is used to build a UAV redeployment optimization model under preset constraints in the discrete event system with the goal of maximizing the system utility of the UAV cluster under the current redeployment task; The third UAV communication deployment and scheduling module for the on-the-spot adjustment task is used to solve the UAV redeployment optimization model, obtain the optimal redeployment strategy of the UAV cluster under the current redeployment task, and schedule the UAVs in the UAV cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any of the above-mentioned methods for deploying and scheduling unmanned aerial vehicle communications for on-the-spot adjustment tasks.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for deploying and scheduling unmanned aerial vehicle communications for on-the-spot adjustment tasks as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for deploying and scheduling unmanned aerial vehicle communications for on-the-spot adjustment tasks.

[0017] The communication deployment and scheduling method for unmanned aerial vehicle (UAV) for ad hoc adjustment tasks provided by the present invention constructs a discrete event system corresponding to each UAV based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, thereby improving the response speed to sudden dynamic demands. Under this discrete event system, by constructing an optimization model for the redeployment of UAVs under preset constraints, the system utility of the UAV cluster under the current redeployment task is maximized. The system utility comprehensively considers the total link capacity, communication rate variance and total energy consumption of the UAV cluster, so that when optimizing the deployment of UAVs, not only the communication capacity but also the stability and energy efficiency of the communication rate are considered, thereby improving the stability and quality of the overall communication service. In addition, when solving the optimization model, by presetting constraints, such as limiting the bandwidth allocated to the user terminal by the UAV, ensuring that the communication link capacity meets the minimum target requirements, and ensuring that the remaining energy consumption of the UAV after executing the task is not less than the minimum energy consumption limit, the energy efficiency and endurance of the UAV when executing the task are ensured, the service interruption problem caused by power exhaustion is avoided, and the performance and reliability of the UAV when executing long-term tasks or complex and changeable scenes are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1This is one of the application scenario schematic diagrams of the UAV communication deployment and scheduling system for ad hoc adjustment tasks provided by the present invention; Figure 2 It is a flow chart of a method for deploying and scheduling UAV communications for on-the-spot adjustment tasks provided by the present invention; Figure 3 This is the second schematic diagram of the application scenario of the UAV communication deployment and scheduling system for the ad hoc adjustment task provided by the present invention; Figure 4 It is a schematic diagram of a scenario of collaborative multi-agent reinforcement learning provided by the present invention; Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In order to better understand the embodiments of the present invention, firstly, relevant contents involved in the embodiments of the present invention are explained accordingly.

[0022] like Figure 1 As shown in FIG. 1 , it is one of the scenario schematic diagrams of the UAV communication deployment and scheduling system for the ad hoc adjustment task provided by the present invention. Figure 1 As shown, the set of UAVs randomly dispersed in the area to be covered by emergency communication is expressed as These drones act as temporary edge base stations, providing services on demand for unevenly distributed ground user terminals. The set of user terminals is represented by , any user terminal The position is represented by .

[0023] In order to better perform key tasks in emergency wireless networks, such as voice calls, real-time video, map construction, object recognition, etc., user terminals may have different communication service quality requirements. The deployment strategy of all drones can be expressed as , among which The deployment location of the drone is .

[0024] In this embodiment, the one-to-many association between the drone and the user terminal is also taken into account, and the user terminal-drone association function is defined in the following form: ; Furthermore, the user terminal-drone association matrix It can be defined as: ; In this embodiment, the purpose is to achieve continuous coverage of ground user terminals by replacing as few drones as possible without interrupting communication services, thereby ensuring the service quality of ground user terminals and improving system energy efficiency.

[0025] Figure 2 : is a flow chart of the UAV communication deployment and scheduling method for ad hoc adjustment tasks provided by the present invention, such as Figure 2 As shown, the method includes the following: Step 210, constructing a discrete event system corresponding to each drone based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, wherein the event space includes at least one event that affects the state of the drone, and the state space includes the state of the drone after the event in the event space occurs; Known drone or The location of each user terminal can be known at any time based on the global positioning system. In this process, for the application scenario of the system, it is necessary to ensure that the coverage area is not too large (exceeding the controllable distance of the normal operation of small drones) to ensure the high flexibility of the drone dynamic operation and maintenance system.

[0026] In this embodiment, the predefined drone event space Mainly includes: target deployment location update event , Arrival at deployment location event , perform redeployment algorithm operation events after power check , Stay in place, deployment status unchanged event , Power reaches the warning threshold event , Return Event (The current mission ends and the drone returns to the home for charging, or all deployment missions end and the drone returns to the home), start charging event , Charging completion event .

[0027] Among them, the state space of the predefined drone It consists of the set of all possible values ​​of the following vector: ; in, Indicates The drone The state after an event occurs. Defined as the following sextuple: ; Here, the element , represents the The drone The flight status after an event occurs, such as whether the drone is currently providing services to ground user terminals, or has not yet departed and is waiting for deployment at the base station; Indicates The drone The current remaining power stage after an event occurs; element Indicates The drone The original deployment location before the event occurred, element Indicates The drone New deployment location, element before the event Indicates The drone The set of all user terminals currently in service after an event occurs is used to determine the communication link relationship between the UAV and the ground user terminal; element Indicates The drone The bandwidth capacity used after the event occurs, Indicates the maximum bandwidth capacity that can be allocated. , Indicates The drone is assigned to The bandwidth of each user terminal.

[0028] Specifically, in this embodiment, To represent the state set of the drone considered in the embodiment of the present invention, the state variable Indicates drone Did the following occur? when : Idle and fully charged; when : Flying; when : Working; when : Waiting to be replaced, about to return; when : The drone battery reaches the warning threshold; when : Charging; when : Waiting for new deployment location to be assigned.

[0029] Furthermore, in this embodiment, the state space is also defined , Event Space For the state transition rules between Figure 3 As shown, if the drone is in In the event of an incident and After that, the state transition can be triggered. Specifically, when an event occurs After that, the state changes to , in the occurrence After the event, the state changes to In this embodiment, the state transition rules are specifically referred to Figure 3 As shown, no further details are given here.

[0030] See also Figure 3 From the state transition rules shown, it can be seen that not all events are feasible when the drone is in any state. In some states, the drone will not participate in the overall deployment and replacement events.

[0031] In this embodiment, each drone is regarded as an independent discrete event system. Each discrete event system is based on the above series of predefined state spaces. , Event Space And state transition rules. Given the collaborative nature of drone clusters, when any drone in a drone cluster triggers a state transition due to an event, the state transition may also trigger the state linkage adjustment of other drones in the drone cluster. That is, other drones in the drone cluster will selectively trigger the state transition of drones based on their own current state and the coordination strategy of the entire cluster formulated according to the current main target task. The linkage between state transitions reflects the complex interactivity of drone clusters, that is, the change of a single drone's state may affect the state and behavior of other drones through the communication and coordination mechanism within the cluster.

[0032] Step 220, under the discrete event system, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, construct a drone redeployment optimization model under preset constraints; wherein the system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the drone cluster.

[0033] In this embodiment, in order to control the number of drones in the air to remain unchanged, under the premise of ensuring that the communication of users connected to the original drones is not interrupted as much as possible, consider reallocating the user terminals originally served by the current low-battery drone to other drones, so that the low-battery drones can return. Under the premise of ensuring user connection, the lower the lower limit of the user's acceptable communication service quality, the larger the drone coverage, and the fewer drones that need to be used or deployed in the overall deployment. The deployment cycle is defined as the start of each global replacement check to the next re-check. During the entire deployment process, set the total A total of 1,000 drones participated Secondary redistribution provides services to all users.

[0034] In each reallocation, in order to ensure the user communication experience, it is necessary to consider the overall user communication rate, the fairness of the user communication rate, and the energy consumption of the drone group. System Utility of Secondary Redistribution Defined as: ; in, represents the total link capacity of the UAV cluster, represents the communication rate variance of the drone cluster, represents the total energy consumption of the drone cluster, They represent the corresponding importance coefficients respectively.

[0035] Here, the total link capacity refers to the total transmission capacity of the communication links between all drones in the drone cluster and the user terminals served by the drones. The communication rate variance measures the degree of difference in the communication rates between all communication links in the drone cluster. The total energy consumption refers to the total energy consumed by the drone cluster during the mission. Drones are usually powered by batteries, so energy consumption is also an important factor limiting their endurance.

[0036] The drone redeployment optimization model constructed in this embodiment aims to find a redeployment strategy that can maximize the system utility while satisfying preset constraints (such as the location of the drone, communication range, energy consumption limit, etc.).

[0037] The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a working drone before executing the current redeployment task, and the second drone is a fully charged drone at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0038] Specifically, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, the drone redeployment optimization model under preset constraints can be expressed as: ; ; ; ; ; ; ; ; Here, constraint C1 means that each user terminal can be served by at most one drone; constraint C2 means that bandwidth can be allocated only when a connection is established between the drone and the user terminal, and the bandwidth allocated by the drone to the user terminal is Not greater than the maximum bandwidth limit ; Constraint C3 means that when the drone provides service, it is in state The sum of bandwidth allocated by the drone to all the terminals it serves cannot exceed its maximum bandwidth limit. ,in, is a binary variable with a value of 0 or 1, When the value is 0, it means the drone has not taken off. When the value is 1, it means that the UAV has set off; constraint C4 represents the communication link capacity between the UAV and the user terminal served by the UAV Not less than the minimum target communication link capacity of the user terminal served by the UAV ; Constraint C5 means that the state The first drone The first remaining energy consumption after executing the current redeployment task Not less than the minimum energy consumption limit , in state The second drone The second remaining energy consumption after executing the current redeployment task Not less than the minimum energy consumption limit , among which, the first drone The second drone is the drone that was working before the current redeployment mission. The fully-charged drones that were at the central base station before the current redeployment mission was performed; constraint C6 means that in order to ensure that the drones are active within the preset controllable position range, the deployment positions of all drones are restricted to a rectangular area with a length of a and a width of b; constraint C7 means that the distance between any two drones is not less than the minimum distance limit .

[0039] Step 230, solving the drone redeployment optimization model to obtain the optimal redeployment strategy of the drone cluster under the current redeployment task, and scheduling the drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy; In this embodiment, an optimization solver can be used to solve the optimization problem of the drone redeployment optimization model. By using the optimization solver, the optimal solution or approximate optimal solution that satisfies all constraints can be efficiently found. The solution result will directly output the system utility. The optimal solution value and the corresponding decision variables: total link capacity , Communication rate variance , total energy consumption Based on the total link capacity , Communication rate variance , total energy consumption The value of can determine the optimal redeployment strategy of the drone cluster, that is, the state transition of each drone behind the drone cluster and the events that occur under this state transition. Finally, based on the obtained optimal redeployment strategy, the drones in the drone cluster can be scheduled to perform the current redeployment task.

[0040] The communication deployment and scheduling method for unmanned aerial vehicle (UAV) for ad hoc adjustment tasks provided by the present invention constructs a discrete event system corresponding to each UAV based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, thereby improving the response speed to sudden dynamic demands. Under this discrete event system, by constructing an optimization model for the redeployment of UAVs under preset constraints, the system utility of the UAV cluster under the current redeployment task is maximized. The system utility comprehensively considers the total link capacity, communication rate variance and total energy consumption of the UAV cluster, so that when optimizing the deployment of UAVs, not only the communication capacity but also the stability and energy efficiency of the communication rate are considered, thereby improving the stability and quality of the overall communication service. In addition, when solving the optimization model, by presetting constraints, such as limiting the bandwidth allocated to the user terminal by the UAV, ensuring that the communication link capacity meets the minimum target requirements, and ensuring that the remaining energy consumption of the UAV after executing the task is not less than the minimum energy consumption limit, the energy efficiency and endurance of the UAV when executing the task are ensured, the service interruption problem caused by power exhaustion is avoided, and the performance and reliability of the UAV when executing long-term tasks or complex and changeable scenes are improved.

[0041] In some embodiments, the communication link capacity between the drone and the user terminal served by the drone can be obtained by: Based on the straight-line distance between the UAV and the user terminal served by the UAV, the line-of-sight loss and non-line-of-sight loss between the UAV and the user terminal served by the UAV are obtained respectively; Based on the horizontal distance between the UAV and the user terminal served by the UAV and the flight altitude of the UAV, the probability of line-of-sight loss between the UAV and the user terminal served by the UAV is obtained; Based on the line-of-sight loss, the non-line-of-sight loss and the line-of-sight loss probability, obtaining an average path loss between the drone and a user terminal served by the drone; Based on the bandwidth allocated by the drone to the user terminal and the average path loss, the communication link capacity between the drone and the user terminal served by the drone is obtained.

[0042] Specifically, it is assumed that each drone can serve multiple user terminals, and each user terminal can only connect to one drone. The first drone The horizontal distance between user terminals and straight-line distance They are given by the following formulas: ; ; Assume the drone is flying at Constant value during flight , then the new deployment location of the drone With the original deployment location The horizontal distance between them is: ; In this embodiment, the viewing distance and non-line-of-sight The channel model is the path loss channel model, then the UAV and users Line-of-sight path loss and non-line-of-sight path loss It can be expressed as: ; ; in, represents the free space path loss, Indicates the carrier frequency, represents the free space reference distance, represents the speed of light, represents the line-of-sight loss parameter, represents the non-line-of-sight loss parameter, Indicates The first drone The straight-line distance between user terminals, is a Gaussian random variable representing line-of-sight loss shadow fading, is a Gaussian random variable representing non-line-of-sight loss shadow fading.

[0043] In addition, The first drone The probability of line-of-sight loss between user terminals for: ; in, and represents two environmental parameter constants, Depends on the height of the building, Depends on the density of the building, Indicates The first drone The elevation angle between user terminals can be calculated as ; Similarly, The first drone The probability of non-line-of-sight loss between user terminals Therefore, the The first drone The average path loss between user terminals can be expressed as: ; In this embodiment, the downlink transmission power of each drone is , then according to Shannon's formula, The first drone The communication link capacity between user terminals for: ; in, Indicates The drone is assigned to The bandwidth of each user terminal, is the variance of the receiver's Gaussian white noise.

[0044] In some embodiments, the minimum energy consumption limit is obtained by: Based on the horizontal flight speed of the UAV, the descent speed, the position of the UAV and the position of the central base station, the energy consumption of the first return flight of the UAV is obtained; Based on the first hovering energy consumption, the moving consumption and the first return energy consumption, the second return energy consumption of the drone is obtained, wherein the hovering energy consumption is the energy consumed by the drone when it performs a redeployment algorithm calculation after the power check, and the moving consumption is the energy consumed by the drone when it moves at a preset maximum moving distance; Based on the second return energy consumption and the second hovering energy consumption, the minimum energy consumption limit of the drone is obtained, where the second hovering energy consumption is the energy consumed when the drone hovers to serve the user terminal.

[0045] In this embodiment, the first return energy consumption It is defined as the minimum power limit of the charging center that can meet the needs of the drone to return and descend to the central base station. Specifically, it can be obtained by the following formula: ; The location of the central base station is , the drone’s position is ; Indicates the horizontal flight speed of the drone. Indicates the vertical flight speed of the drone, Indicates the flight altitude of the drone.

[0046] Second return energy consumption Refers to drones The remaining can also support drones while meeting the return flight requirements The minimum energy boundary of waiting for other drones to replace and serve the user terminal that has been unloaded, and then returning to charge, can be obtained by the following formula: ; in, It represents the energy consumed by the drone when it performs the redeployment algorithm after the battery check. It is expressed as the energy consumed by the drone when moving at the preset maximum moving distance.

[0047] Here, , = , It represents the average time for all drones to perform the redeployment algorithm after the battery check. It is expressed as the maximum value of the time required for all drones to move from their original deployment positions to their new deployment positions. Expressed as drone power.

[0048] Minimum energy consumption limit It refers to the power limit of the drone that can support the movement from the original location to the new deployment point and continue to serve until the next low-battery check event occurs. Specifically, it can be obtained by the following formula: ; in, Expressed as the second hovering energy consumption, , It is a constant representing the maximum time between each power check.

[0049] In some embodiments, the method further comprises: Based on the communication link capacity between the drones and the user terminals served by the drones, the total communication link capacity of the drone cluster is obtained; Based on the communication link capacity and the minimum target communication link capacity of the user terminal served by the drone, obtaining the communication rate variance of the drone cluster; Based on a first total energy consumption of a first drone and a second total energy consumption of a second drone in the drone cluster, the total energy consumption of the drone cluster is obtained.

[0050] In this embodiment, the total communication link capacity of the drone cluster refers to the sum of the communication link capacities between all drones and the user terminals they serve, which is usually based on the communication link capacity between each drone and the user terminal. Specifically, the total communication link capacity of the UAV cluster is It can be obtained by the following formula: .

[0051] The communication rate variance is an indicator to measure the difference in communication rates between different drones in a drone cluster and user terminals. Based on the communication link capacity between each drone and the user terminal and the minimum target communication link capacity required by the user terminal, the deviation between the communication rate provided by each drone and the user terminal requirement can be calculated. Then, by statistically analyzing these deviations, the communication rate variance of the drone cluster can be obtained. Specifically, the communication rate variance of the drone cluster is It can be obtained by the following formula: ; in, , .

[0052] The total energy consumption of a drone cluster refers to the total energy consumed by all drones participating in the redeployment task when performing the redeployment task. Here, the total energy consumption calculation of a drone cluster involves two types of drones: one is the drone that was working before performing the current redeployment task (the first drone). ), and the other is a fully charged drone that was at the central base station before executing the current redeployment mission (the second drone ). By calculating the total energy consumption of these two types of drones separately and adding them together, the total energy consumption of the drone cluster can be obtained.

[0053] Specifically, the first total energy consumption of the first UAV and the second total energy consumption of the second UAV are obtained in the following manner: Obtaining a first total energy consumption of the first drone based on a first duration corresponding to executing a redeployment algorithm operation after the first drone performs a power check, a second duration corresponding to the first drone moving from an original deployment position to a new deployment position, and the drone power; Based on a third time duration corresponding to the second UAV moving from the original deployment position to the new deployment position and the UAV power, a second total energy consumption of the second UAV is obtained.

[0054] It should be understood that since the first drone was previously in a working state, before executing the redeployment algorithm, the first drone will first be checked for power, and then the first drone will execute the redeployment algorithm, which also takes a certain amount of time. Therefore, in this embodiment, the duration required for this process is referred to as the first duration. After completing the algorithm operation, the first drone needs to move from its original deployment position to the new deployment position, and this movement process takes a second duration.

[0055] In this embodiment, the total energy consumption of the first drone to complete the redeployment task is calculated by comprehensively considering the first duration, the second duration and the drone power, that is, the first total energy consumption. The first total energy consumption includes the energy consumption during the execution of the redeployment algorithm after the power check and the energy consumption during the flight.

[0056] Specifically, the first total energy consumption can be expressed as ,in, represents the number of the first UAV, P represents the UAV power, Indicates the first duration, Indicates the second duration.

[0057] Here, the first duration The average time for executing the redeployment algorithm operation after the battery check of all the first drones can be It can be the maximum value of the time taken by all first drones to move from the original deployment position to the new deployment position.

[0058] For the second drone, since the second drone will fly from the central base station to the destination after completing the redeployment algorithm operation, its energy consumption calculation is mainly based on the third time required to move from the original deployment location (i.e., the central base station) to the new deployment location. Similarly, combining the third time and the drone power, the second total energy consumption of the second drone to complete the mobile task can be calculated.

[0059] Here, the second total energy consumption can be expressed as ,in, is a binary variable with a value of 0 or 1, When the value is 0, it means the drone has not taken off. When the value is 1, it means the drone has set off. The third duration.

[0060] Similarly, in this embodiment, the third duration It can be the maximum value of the time required for all second drones to move from the original deployment position to the new deployment position.

[0061] Furthermore, the second duration corresponding to the first UAV moving from the original deployment position to the new deployment position and the third duration corresponding to the second UAV moving from the original deployment position to the new deployment position are obtained in the following manner: Based on the horizontal flight speed of the first drone, the original deployment position of the first drone, and the new deployment position of the first drone, obtaining a second time duration corresponding to the first drone moving from the original deployment position to the new deployment position; Based on the horizontal flight speed of the second UAV, the original deployment position of the second UAV, the new deployment position of the second UAV, the vertical flight speed of the second UAV and the flight altitude of the second UAV, the third time duration corresponding to the second UAV moving from the original deployment position to the new deployment position is obtained.

[0062] Specifically, the second duration It can be obtained by the following formula: ; The third duration It can be obtained by the following formula: ; in, Indicates the original deployment position of the drone, represents the new deployment location of the drone, Indicates the horizontal flight speed of the drone. Indicates the vertical flight speed of the drone, Indicates the flight altitude of the drone.

[0063] The following is a description of the UAV communication deployment and scheduling device for ad hoc adjustment tasks provided by the present invention. The UAV communication deployment and scheduling device for ad hoc adjustment tasks described below and the UAV communication deployment and scheduling method for ad hoc adjustment tasks described above can be referenced to each other.

[0064] In this embodiment, the UAV communication deployment and scheduling device for the on-the-spot adjustment task includes: A first UAV communication deployment and scheduling module 410 for on-the-spot adjustment tasks is used to construct a discrete event system corresponding to each UAV based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, wherein the event space includes at least one event that affects the state of the UAV, and the state space includes the state of the UAV after the event in the event space occurs; The second UAV communication deployment and scheduling module 420 for the ad hoc adjustment task is used to build a UAV redeployment optimization model under preset constraints in the discrete event system with the goal of maximizing the system utility of the UAV cluster under the current redeployment task; The third UAV communication deployment and scheduling module 430 for the on-the-spot adjustment task is used to solve the UAV redeployment optimization model, obtain the optimal redeployment strategy of the UAV cluster under the current redeployment task, and schedule the UAVs in the UAV cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0065] The unmanned aerial vehicle communication deployment and scheduling device for the ad hoc adjustment task provided by the present invention constructs a discrete event system corresponding to each unmanned aerial vehicle based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, thereby improving the response speed to sudden dynamic demands. Under this discrete event system, by constructing an unmanned aerial vehicle redeployment optimization model under preset constraints, the system utility of the unmanned aerial vehicle cluster under the current redeployment task is maximized. The system utility comprehensively considers the total link capacity, communication rate variance and total energy consumption of the unmanned aerial vehicle cluster, so that when optimizing the deployment of unmanned aerial vehicles, not only the communication capacity but also the stability and energy efficiency of the communication rate are considered, thereby improving the stability and quality of the overall communication service. In addition, when solving the optimization model, by presetting constraints, such as limiting the bandwidth allocated to the user terminal by the unmanned aerial vehicle, ensuring that the communication link capacity meets the minimum target requirements, and ensuring that the remaining energy consumption of the unmanned aerial vehicle after executing the task is not less than the minimum energy consumption limit, the energy efficiency and endurance of the unmanned aerial vehicle when executing the task are ensured, the service interruption problem caused by power exhaustion is avoided, and the performance and reliability of the unmanned aerial vehicle in performing long-term tasks or complex and changeable scenes are improved.

[0066] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communications interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the UAV communication deployment and scheduling method for the ad hoc adjustment task, and the method includes: Based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, a discrete event system corresponding to each drone is constructed, wherein the event space includes at least one event that affects the state of the drone, and the state space includes the state of the drone after the event in the event space occurs; Under the discrete event system, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, a drone redeployment optimization model under preset constraints is constructed; Solving the drone redeployment optimization model to obtain the optimal redeployment strategy of the drone cluster under the current redeployment task, and scheduling the drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0067] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0068] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the UAV communication deployment and scheduling method for the ad hoc adjustment task provided by the above methods, the method includes: Based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, a discrete event system corresponding to each drone is constructed, wherein the event space includes at least one event that affects the state of the drone, and the state space includes the state of the drone after the event in the event space occurs; Under the discrete event system, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, a drone redeployment optimization model under preset constraints is constructed; Solving the drone redeployment optimization model to obtain the optimal redeployment strategy of the drone cluster under the current redeployment task, and scheduling the drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0069] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the UAV communication deployment and scheduling method for the ad hoc adjustment task provided by the above methods, the method comprising: Based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, a discrete event system corresponding to each drone is constructed, wherein the event space includes at least one event that affects the state of the drone, and the state space includes the state of the drone after the event in the event space occurs; Under the discrete event system, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, a drone redeployment optimization model under preset constraints is constructed; Solving the drone redeployment optimization model to obtain the optimal redeployment strategy of the drone cluster under the current redeployment task, and scheduling the drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

[0070] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0071] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 deploying and scheduling UAV communications for on-the-spot adjustment tasks, characterized in that: The method comprises: Based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, a discrete event system corresponding to each drone is constructed, wherein the event space includes at least one event that affects the state of the drone, and the state space includes the state of the drone after the event in the event space occurs; Under the discrete event system, with the goal of maximizing the system utility of the drone cluster under the current redeployment task, a drone redeployment optimization model under preset constraints is constructed; Solving the drone redeployment optimization model to obtain an optimal redeployment strategy for the drone cluster under the current redeployment task, and scheduling drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

2. The method for deploying and scheduling UAV communications for on-the-spot adjustment tasks according to claim 1 is characterized in that: The communication link capacity between the UAV and the user terminal served by the UAV is obtained in the following way: Based on the straight-line distance between the UAV and the user terminal served by the UAV, the line-of-sight loss and non-line-of-sight loss between the UAV and the user terminal served by the UAV are obtained respectively; Based on the horizontal distance between the UAV and the user terminal served by the UAV and the flight altitude of the UAV, the probability of line-of-sight loss between the UAV and the user terminal served by the UAV is obtained; Based on the line-of-sight loss, the non-line-of-sight loss and the line-of-sight loss probability, obtaining an average path loss between the drone and a user terminal served by the drone; Based on the bandwidth allocated by the drone to the user terminal and the average path loss, the communication link capacity between the drone and the user terminal served by the drone is obtained.

3. The method for deploying and scheduling UAV communication for on-the-spot adjustment tasks according to claim 1 is characterized in that: The method further comprises: Based on the communication link capacity between the drones and the user terminals served by the drones, the total communication link capacity of the drone cluster is obtained; Based on the communication link capacity and the minimum target communication link capacity of the user terminal served by the drone, obtaining the communication rate variance of the drone cluster; Based on a first total energy consumption of a first drone and a second total energy consumption of a second drone in the drone cluster, the total energy consumption of the drone cluster is obtained.

4. The method for deploying and scheduling UAV communications for ad hoc adjustment tasks according to claim 3 is characterized in that: The first total energy consumption of the first UAV and the second total energy consumption of the second UAV are obtained in the following manner: Obtaining a first total energy consumption of the first drone based on a first duration corresponding to executing a redeployment algorithm operation after the first drone performs a power check, a second duration corresponding to the first drone moving from an original deployment position to a new deployment position, and the drone power; Based on a third time duration corresponding to the second UAV moving from the original deployment position to the new deployment position and the UAV power, a second total energy consumption of the second UAV is obtained.

5. The method for deploying and scheduling UAV communication for on-the-spot adjustment tasks according to claim 4 is characterized in that: The second duration corresponding to the first UAV moving from the original deployment position to the new deployment position and the third duration corresponding to the second UAV moving from the original deployment position to the new deployment position are obtained in the following manner: Based on the horizontal flight speed of the first drone, the original deployment position of the first drone, and the new deployment position of the first drone, obtaining a second time duration corresponding to the first drone moving from the original deployment position to the new deployment position; Based on the horizontal flight speed of the second UAV, the original deployment position of the second UAV, the new deployment position of the second UAV, the vertical flight speed of the second UAV and the flight altitude of the second UAV, the third time duration corresponding to the second UAV moving from the original deployment position to the new deployment position is obtained.

6. The method for deploying and scheduling UAV communications for on-the-spot adjustment tasks according to claim 1 is characterized in that: The minimum energy consumption limit is obtained by: Based on the horizontal flight speed of the UAV, the descent speed, the position of the UAV and the position of the central base station, the energy consumption of the first return flight of the UAV is obtained; Based on the first hovering energy consumption, the moving consumption and the first return energy consumption, the second return energy consumption of the drone is obtained, wherein the hovering energy consumption is the energy consumed by the drone when it performs a redeployment algorithm calculation after the power check, and the moving consumption is the energy consumed by the drone when it moves at a preset maximum moving distance; Based on the second return energy consumption and the second hovering energy consumption, the minimum energy consumption limit of the drone is obtained, where the second hovering energy consumption is the energy consumed when the drone hovers to serve the user terminal.

7. A UAV communication deployment and scheduling device for ad hoc adjustment tasks, characterized in that: The device comprises: A first UAV communication deployment and scheduling module for on-the-spot adjustment tasks, configured to construct a discrete event system corresponding to each UAV based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, wherein the event space includes at least one event that affects the state of the UAV, and the state space includes the state of the UAV after the event in the event space occurs; The second UAV communication deployment and scheduling module for the ad hoc adjustment task is used to build a UAV redeployment optimization model under preset constraints in the discrete event system with the goal of maximizing the system utility of the UAV cluster under the current redeployment task; The third UAV communication deployment and scheduling module for the on-the-spot adjustment task is used to solve the UAV redeployment optimization model, obtain the optimal redeployment strategy of the UAV cluster under the current redeployment task, and schedule the UAVs in the UAV cluster to perform the current redeployment task based on the optimal redeployment strategy; The system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster; The preset constraint condition includes at least one of the following: A user terminal is served by at most one drone in the drone cluster; The bandwidth allocated to user terminals by drones in a drone cluster shall not exceed the maximum bandwidth limit; The total bandwidth allocated to the working drones in the drone cluster shall not exceed the maximum bandwidth limit; The communication link capacity between the drones in the drone cluster and the user terminals served by the drones shall not be less than the minimum target communication link capacity of the user terminals served by the drones; The first remaining energy consumption of the first drone in the drone cluster that is in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than the minimum energy consumption limit, and the second remaining energy consumption of the second drone in the drone cluster that is idle and fully charged after executing the current redeployment task is not less than the minimum energy consumption limit, the first drone is a drone that is working before executing the current redeployment task, and the second drone is a fully charged drone that is at the central base station before executing the current redeployment task; The drones in the drone cluster are within the preset controllable position range; The distance between any two drones in a drone cluster shall not be less than the minimum distance limit.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the UAV communication deployment and scheduling method for on-the-spot adjustment tasks as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the UAV communication deployment and scheduling method for ad hoc adjustment tasks as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by the processor, it implements the UAV communication deployment and scheduling method for ad hoc adjustment tasks as described in any one of claims 1 to 6.

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