Unmanned aerial vehicle communication deployment and scheduling method for on-the-fly adjustment task

Abstract

The application provides a UAV communication deployment and scheduling method for on-the-spot adjustment tasks, comprising: constructing 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; under the discrete event system, constructing a UAV redeployment optimization model under a preset constraint condition, with the goal of maximizing the system utility of a UAV cluster under a current redeployment task; solving the UAV redeployment optimization model to obtain an optimal redeployment strategy of the UAV cluster under the current redeployment task, and scheduling the UAVs in the UAV cluster to execute the current redeployment task based on the optimal redeployment strategy; the system utility is obtained based on the total link capacity, the communication rate variance, and the total energy consumption of the UAV cluster. The application improves the agility and flexibility of the response to emergencies, improves the stability of the communication service, and improves the reliability of the UAVs in executing long-time tasks or in complex and variable scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a UAV communication deployment and scheduling method for on-the-spot task adjustment. BACKGROUND

[0002] Unmanned aerial vehicles (UAVs) have advantages such as low cost, high flexibility, strong independence, and possibly higher line-of-sight in wireless communication systems. Compared with traditional methods such as satellite communication and ground emergency communication vehicles, UAV-based aerial base stations have advantages such as low cost, no terrain restrictions, and no dependence on ground stations in emergency communication deployment, and can meet the flexible communication needs of users, timely deploy, and enhance communication capacity and network robustness.

[0003] However, existing UAV communication deployment has difficulty in meeting the rapid response of the system to sudden dynamic needs when facing actual application scenarios. Although UAVs have many advantages in wireless communication, current deployment strategies often fail to fully consider the limited energy of UAVs in real environments and the time-varying nature of user demand, which limits the immediate adaptability and flexibility of UAV communication systems. In addition, current deployment strategies do not consider the sustainability of UAV systems, affecting the efficiency and reliability of UAVs in long-term task execution or complex and variable scenarios.

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

[0005] The present application provides a UAV communication deployment and scheduling method for on-the-spot task adjustment to solve the defects of existing UAV communication deployment.

[0006] The present application provides a UAV communication deployment and scheduling method for on-the-spot task adjustment, comprising the following steps:

[0007] 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 UAV is constructed, the event space includes at least one event affecting the state of the UAV, and the state space includes the state of the UAV after the occurrence of the event in the event space;

[0008] Under the discrete event system, a UAV redeployment optimization model under a preset constraint condition is constructed to maximize the system utility of the UAV cluster under the current redeployment task;

[0009] Solving the unmanned aerial vehicle redeployment optimization model obtains an optimal redeployment strategy of the unmanned aerial vehicle cluster under the current redeployment task, and the unmanned aerial vehicles in the unmanned aerial vehicle cluster are dispatched to execute the current redeployment task based on the optimal redeployment strategy;

[0010] The system utility is obtained based on total link capacity, communication rate variance and total energy consumption of the unmanned aerial vehicle cluster.

[0011] The preset constraint condition comprises at least one of the following:

[0012] The user terminal is served by at most one unmanned aerial vehicle in the unmanned aerial vehicle cluster;

[0013] The bandwidth allocated to the user terminal by the unmanned aerial vehicle in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit value;

[0014] The total bandwidth allocated by the unmanned aerial vehicle in the unmanned aerial vehicle cluster in a working state is not greater than a maximum bandwidth limit value;

[0015] The communication link capacity between the unmanned aerial vehicle in the unmanned aerial vehicle cluster and the user terminal served by the unmanned aerial vehicle is not less than a minimum target communication link capacity of the user terminal served by the unmanned aerial vehicle;

[0016] The first residual energy of a first unmanned aerial vehicle in the unmanned aerial vehicle cluster in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than a minimum energy consumption limit value, and the second residual energy of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster in an idle and sufficient power state after executing the current redeployment task is not less than the minimum energy consumption limit value, the first unmanned aerial vehicle being a working unmanned aerial vehicle before executing the current redeployment task, and the second unmanned aerial vehicle being a full-power unmanned aerial vehicle at the central base station before executing the current redeployment task;

[0017] The unmanned aerial vehicle in the unmanned aerial vehicle cluster is in a preset controllable position range;

[0018] The distance between any two unmanned aerial vehicles in the unmanned aerial vehicle cluster is not less than a minimum distance limit value.

[0019] According to the unmanned aerial vehicle communication deployment and scheduling method for temporary adjustment tasks provided by the application, the communication link capacity between the unmanned aerial vehicle and the user terminal served by the unmanned aerial vehicle is obtained by the following method:

[0020] Based on the straight-line distance between the unmanned aerial vehicle and the user terminal served by the unmanned aerial vehicle, the line-of-sight loss and the non-line-of-sight loss between the unmanned aerial vehicle and the user terminal served by the unmanned aerial vehicle are obtained respectively;

[0021] Based on the horizontal distance between the unmanned aerial vehicle and the user terminal served by the unmanned aerial vehicle and the flight height of the unmanned aerial vehicle, the line-of-sight loss probability between the unmanned aerial vehicle and the user terminal served by the unmanned aerial vehicle is obtained;

[0022] Based on the line-of-sight loss, the non-line-of-sight loss and the line-of-sight loss probability, an average path loss between the UAV and the user terminal served by the UAV is obtained;

[0023] Based on the bandwidth allocated by the UAV to the user terminal and the average path loss, a communication link capacity between the UAV and the user terminal served by the UAV is obtained.

[0024] According to the present application, a UAV communication deployment and scheduling method for on-the-fly task adjustment is provided, and the method further comprises:

[0025] Based on the communication link capacity between the UAV and the user terminal served by the UAV, a total communication link capacity of the UAV cluster is obtained;

[0026] Based on the communication link capacity and the minimum target communication link capacity of the user terminal served by the UAV, a communication rate variance of the UAV cluster is obtained.

[0027] Based on the first total energy consumption of the first UAV and the second total energy consumption of the second UAV in the UAV cluster, a total energy consumption of the UAV cluster is obtained.

[0028] According to the present application, a UAV communication deployment and scheduling method for on-the-fly task adjustment is provided, and 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:

[0029] Based on a first time length corresponding to the execution of the redeployment algorithm operation after the first UAV performs the power check, a second time length corresponding to the movement of the first UAV from the original deployment position to the new deployment position, and the UAV power, the first total energy consumption of the first UAV is obtained.

[0030] Based on a third time length corresponding to the movement of the second UAV from the original deployment position to the new deployment position, and the UAV power, the second total energy consumption of the second UAV is obtained.

[0031] According to the present application, a UAV communication deployment and scheduling method for on-the-fly task adjustment is provided, and the second time length corresponding to the movement of the first UAV from the original deployment position to the new deployment position and the third time length corresponding to the movement of the second UAV from the original deployment position to the new deployment position are obtained by the following method:

[0032] Based on the horizontal flight speed of the first UAV, the original deployment position of the first UAV, and the new deployment position of the first UAV, the second time length corresponding to the movement of the first UAV from the original deployment position to the new deployment position is obtained.

[0033] Based on the horizontal flight speed of the second unmanned aerial vehicle, the original deployment position of the second unmanned aerial vehicle, the new deployment position of the second unmanned aerial vehicle, the vertical flight speed of the second unmanned aerial vehicle and the flight height of the second unmanned aerial vehicle, a third time length corresponding to movement of the second unmanned aerial vehicle from the original deployment position to the new deployment position is obtained.

[0034] According to the present application, a minimum energy consumption limit value is obtained by the following method:

[0035] Based on the horizontal flight speed of the unmanned aerial vehicle, the descending aircraft speed, the unmanned aerial vehicle position and the central base station position, a first return energy consumption of the unmanned aerial vehicle is obtained;

[0036] Based on the first hovering energy consumption, the movement consumption and the first return energy consumption, a second return energy consumption of the unmanned aerial vehicle is obtained, the hovering energy consumption is the energy consumed by the unmanned aerial vehicle when performing the re-deployment algorithm operation after the power check, and the movement consumption is the energy consumed by the unmanned aerial vehicle when moving within the preset maximum movement distance;

[0037] Based on the second return energy consumption and the second hovering energy consumption, a minimum energy consumption limit value of the unmanned aerial vehicle is obtained, and the second hovering energy consumption is the energy consumed by the unmanned aerial vehicle when hovering to serve the user terminal.

[0038] The present application also provides a unmanned aerial vehicle communication deployment and scheduling device for on-the-spot adjustment tasks, the device comprises:

[0039] The first unmanned aerial vehicle communication deployment and scheduling module for on-the-spot adjustment tasks is used for constructing a discrete event system corresponding to each unmanned aerial vehicle based on a predefined event space, a state space, a state transition rule corresponding to the event space and the state space, at least one event in the event space affecting the state of the unmanned aerial vehicle, and the state space including the state of the unmanned aerial vehicle after the occurrence of the event in the event space;

[0040] The second unmanned aerial vehicle communication deployment and scheduling module for on-the-spot adjustment tasks is used for constructing a re-deployment optimization model of the unmanned aerial vehicle under a preset constraint condition, with the goal of maximizing the system utility of the unmanned aerial vehicle cluster under the current re-deployment task under the discrete event system;

[0041] The third unmanned aerial vehicle communication deployment and scheduling module for on-the-spot adjustment tasks is used for solving the re-deployment optimization model of the unmanned aerial vehicle, 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;

[0042] The system utility is obtained based on the total link capacity of the unmanned aerial vehicle cluster, the communication rate variance and the total energy consumption;

[0043] The preset constraint condition comprises at least one of the following:

[0044] The user terminal is served by at most one unmanned aerial vehicle in the unmanned aerial vehicle cluster;

[0045] The bandwidth allocated to the user terminal by the unmanned aerial vehicle in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit value;

[0046] The total bandwidth allocated by the unmanned aerial vehicles in the unmanned aerial vehicle cluster in a working state is not greater than a maximum bandwidth limit value;

[0047] The communication link capacity between the unmanned aerial vehicle in the unmanned aerial vehicle cluster and the user terminal served by the unmanned aerial vehicle is not less than a minimum target communication link capacity of the user terminal served by the unmanned aerial vehicle;

[0048] The first residual energy of a first unmanned aerial vehicle in the unmanned aerial vehicle cluster in a state of waiting for allocation of a new deployment position after performing a current redeployment task is not less than a minimum energy consumption limit value, and the second residual energy of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster in an idle and sufficient power state after performing the current redeployment task is not less than the minimum energy consumption limit value, the first unmanned aerial vehicle being an unmanned aerial vehicle working before performing the current redeployment task, and the second unmanned aerial vehicle being a full-power unmanned aerial vehicle at the central base station before performing the current redeployment task;

[0049] The unmanned aerial vehicle in the unmanned aerial vehicle cluster is in a preset controllable position range;

[0050] The distance between any two unmanned aerial vehicles in the unmanned aerial vehicle cluster is not less than a minimum distance limit value.

[0051] The application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the unmanned aerial vehicle communication deployment and scheduling method for on-the-spot adjustment tasks according to any one of the above.

[0052] The application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the unmanned aerial vehicle communication deployment and scheduling method for on-the-spot adjustment tasks according to any one of the above.

[0053] The application also provides a computer program product comprising a computer program, wherein the computer program is executable by a processor to implement the unmanned aerial vehicle communication deployment and scheduling method for on-the-spot adjustment tasks according to any one of the above.

[0054] The present invention provides a method for deploying and scheduling drone communications for on-the-fly adjustment tasks. By constructing a discrete event system for each drone based on predefined event spaces, state spaces, and state transition rules corresponding to the event spaces and state spaces, the system improves response speed to sudden dynamic demands. Within this discrete event system, a drone redeployment optimization model is constructed under preset constraints, aiming to maximize the system utility of the drone cluster under the current redeployment task. System utility comprehensively considers the total link capacity, communication rate variance, and total energy consumption of the drone cluster. This ensures that drone deployment optimization not only considers communication capacity but also communication rate stability and energy efficiency, thereby improving the stability and quality of the overall communication service. Furthermore, when solving the optimization model, by presetting constraints such as limiting the bandwidth allocated to user terminals by drones, ensuring that the communication link capacity meets the minimum target requirement, and ensuring that the remaining energy consumption of drones after completing the mission is not less than the minimum energy consumption limit, the energy efficiency and endurance of drones during mission execution are guaranteed, avoiding service interruptions due to battery exhaustion and improving the efficiency and reliability of drones in long-duration missions or complex and changing scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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 any creative work.

[0056] Figure 1 This is one of the application scenario diagrams of the UAV communication deployment and scheduling system for on-the-spot adjustment tasks provided by the present invention;

[0057] Figure 2 This is a flow chart of a method for deploying and scheduling UAV communications for on-the-fly adjustment tasks provided by the present invention;

[0058] Figure 3 This is the second schematic diagram of an application scenario of the UAV communication deployment and scheduling system for on-the-spot adjustment tasks provided by the present invention;

[0059] Figure 4 This is a schematic diagram of the scenario of collaborative multi-agent reinforcement learning provided by the present invention;

[0060] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0061] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0062] In order to better understand the embodiments of the present invention, relevant contents involved in the embodiments of the present invention are first explained accordingly.

[0063] like Figure 1 As shown in FIG, it is one of the scene 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 randomly dispersed UAVs in the emergency communication coverage area is represented as These drones act as temporary edge base stations, providing services on demand to unevenly distributed ground user terminals. The set of user terminals is represented by , any user terminal The position is represented by .

[0064] 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 .

[0065] In this embodiment, the one-to-many association between drones and user terminals is also taken into account, and the user terminal-drone association function is defined in the following form:

[0066] ;

[0067] Furthermore, the user terminal-UAV association matrix It can be defined as:

[0068] ;

[0069] In this embodiment, the purpose is to achieve continuous coverage of ground user terminals using as few drones as possible without interrupting communication services, thereby ensuring the service quality of ground user terminals and improving system energy efficiency.

[0070] Figure 2 This is a flow chart of the UAV communication deployment and scheduling method for on-the-fly adjustment tasks provided by the present invention, such as Figure 2As shown, the method includes the following:

[0071] Step 210: construct a discrete event system corresponding to each drone based on a predefined event space, a state space, and state transition rules 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;

[0072] 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 this 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.

[0073] In this embodiment, the predefined drone event space Mainly includes: target deployment location update event , Arrival at deployment location event , execute the redeployment algorithm operation event after the power check , Stay in place, deployment status unchanged event , the battery 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 .

[0074] Among them, the state space of the predefined drone consists of the set of all possible values ​​of the following vector:

[0075] ;

[0076] in, Indicates the The drone The state after an event occurs, Defined as the following sextuple:

[0077] ;

[0078] 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 is waiting for deployment at the base station; Indicates the The drone The current remaining power stage after an event occurs; element Indicates the The drone The original deployment location before the event occurred, the element Indicates the The drone New deployment location, element before the event Indicates the 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; Indicates the The drone The bandwidth capacity used after the event occurs, Indicates the maximum bandwidth capacity that can be allocated. , Indicates the The drone is assigned to The bandwidth of each user terminal.

[0079] Specifically, in this embodiment, To represent the state set of the UAV considered in the embodiment of the present invention, the state variable Indicates drone Did the following occur?

[0080] when : Idle and fully charged;

[0081] when : Flying;

[0082] when : working;

[0083] when : Waiting to be replaced, about to return;

[0084] when : The drone's battery level reaches the warning threshold;

[0085] when : Charging;

[0086] when : Waiting for new deployment location to be assigned.

[0087] Furthermore, this embodiment also defines the state space , event space For the state transition rules between Figure 3As shown, if the state of the UAV is in , the state transition can be triggered after the occurrence of an event , and , specifically, after the occurrence of an event , the state is changed to , and after the occurrence of an event , the state is changed to . In this embodiment, the state transition rules are specifically described in Figure 3 , which will not be described in detail here.

[0088] As shown in the state transition rules in Figure 3 , not all events are feasible when the UAV is in any state, and the UAV will not participate in the overall deployment and replacement events in some states.

[0089] In this embodiment, each UAV is considered as an independent discrete event system, and each discrete event system operates according to the above series of predefined state space , event space and state transition rules. Due to the collaborative nature of the UAV cluster, when any UAV in the UAV cluster triggers a state transition due to an event, the state transition may trigger the state linkage adjustment of other UAVs in the UAV cluster. That is, other UAVs in the UAV cluster will selectively trigger the state transition of the UAV according to its current state and the collaborative strategy of the entire cluster formulated according to the current main target task. The linkage between state transitions reflects the complex interaction of the UAV cluster, that is, the change of a single UAV state may affect the state and behavior of other UAVs through the communication and coordination mechanism within the cluster.

[0090] In step 220, under the discrete event system, a UAV redeployment optimization model under a preset constraint condition is constructed with the goal of maximizing the system utility of the UAV cluster under the current redeployment task; wherein the system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the UAV cluster.

[0091] In this embodiment, in order to control the number of UAVs in the air to be constant, under the premise of ensuring that the user communication connected by the original UAV is not interrupted as much as possible, the user terminals originally served by the current low-battery UAV are considered to be redistributed to other UAVs, so that the low-battery UAV returns. Under the premise of ensuring user connection, the lower the lower limit of the user-acceptable communication service quality, the larger the coverage range of the UAV, and the fewer the number of UAVs needed to be used or deployed in the overall deployment. From the start of each global replacement check to the next check, it is defined as a deployment period. In the whole deployment process, a total of UAVs participate in a total of redistribution to provide services for all users.

[0092] In each redistribution, in order to guarantee the user communication experience, the overall user communication rate, the fairness of the user communication rate, and the energy consumption of the UAV swarm need to be considered. In the embodiment, the system utility of the first redistribution is defined as:

[0093] ;

[0094] wherein, represents the total link capacity of the UAV swarm, represents the variance of the communication rate of the UAV swarm, represents the total energy consumption of the UAV swarm, respectively represent the corresponding importance coefficients.

[0095] Here, the total link capacity refers to the total transmission capability of the communication links between all the UAVs in the UAV swarm and the user terminals served by the UAVs. The variance of the communication rate measures the difference degree of the communication rates between all the communication links in the UAV swarm. The total energy consumption refers to the total energy consumed by the UAV swarm in the process of performing the task. The UAV usually relies on battery power supply, and therefore the energy consumption is also an important factor limiting the endurance of the UAV.

[0096] The UAV redistribution optimization model constructed in the embodiment aims to find a redistribution strategy that can maximize the system utility under the premise of meeting the preset constraint conditions (such as the position of the UAV, the communication range, the energy consumption limit, etc.).

[0097] The preset constraint conditions include at least one of the following:

[0098] The user terminal is served by at most one UAV in the UAV swarm;

[0099] The bandwidth allocated to the user terminal by the UAV in the UAV swarm is not greater than the maximum bandwidth limit value;

[0100] The total bandwidth allocated by the UAV in the working state in the UAV swarm is not greater than the maximum bandwidth limit value;

[0101] The communication link capacity between the UAV in the UAV swarm and the user terminal served by the UAV is not less than the minimum target communication link capacity of the user terminal served by the UAV;

[0102] ​​The first residual energy consumption of a first UAV in the UAV cluster in a state of waiting for allocation of a new deployment position after performing the current redeployment task is not less than a minimum energy consumption limit, and the second residual energy consumption of a second UAV in the UAV cluster in an idle and sufficient power state after performing the current redeployment task is not less than the minimum energy consumption limit, the first UAV is a UAV that is working before performing the current redeployment task, and the second UAV is a full-power UAV at the central base station before performing the current redeployment task;

[0103] The UAVs in the UAV cluster are within a preset controllable position range.

[0104] The distance between any two UAVs in the UAV cluster is not less than a minimum distance limit.

[0105] Specifically, to maximize the system utility of the UAV cluster under the current redeployment task, the UAV redeployment optimization model under the preset constraint condition can be expressed as:

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] Here, constraint condition C1 indicates that each user terminal can be served by at most one UAV; constraint condition C2 indicates that bandwidth can be allocated only when the UAV establishes a connection with the user terminal, and the bandwidth allocated by the UAV to the user terminal is not greater than a maximum bandwidth limit ; constraint condition C3 indicates that when the UAV provides services, that is, the UAV in the state allocates the sum of bandwidths to all user terminals served by the UAV, which cannot exceed the maximum bandwidth limit , where is a binary variable with a value of 0 or 1, when the value is 0, it indicates that the UAV has not departed, When the value is 1, it means that the UAV has set off; the 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 in 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 UAV The second drone is the one that was working before the current redeployment mission. The fully charged drones are located at the central base station before the current redeployment mission. 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 a and a width b. Constraint C7 means that the distance between any two drones is not less than the minimum distance limit. .

[0115] Step 230: Solve the drone redeployment optimization model to obtain the optimal redeployment strategy for the drone cluster under the current redeployment task, and schedule the drones in the drone cluster to perform the current redeployment task based on the optimal redeployment strategy.

[0116] In this embodiment, an optimization solver can be used to solve the optimization problem of the UAV redeployment optimization model. By using the optimization solver, the optimal solution or approximate optimal solution that meets 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 for the drone cluster, that is, the state transitions corresponding to each drone after the drone cluster and the events that occur under these state transitions. Finally, based on the obtained optimal redeployment strategy, the drones in the drone cluster can be scheduled to perform the current redeployment task.

[0117] The application provides a UAV communication deployment and scheduling method for on-site adjustment tasks.

[0118] In some embodiments, the communication link capacity between the UAV and the user terminal served by the UAV can be obtained in the following way:

[0119] 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;

[0120] Based on the horizontal distance between the UAV and the user terminal served by the UAV and the flight height of the UAV, the line-of-sight loss probability between the UAV and the user terminal served by the UAV is obtained;

[0121] Based on the line-of-sight loss, the non-line-of-sight loss and the line-of-sight loss probability, the average path loss between the UAV and the user terminal served by the UAV is obtained;

[0122] Based on the bandwidth allocated by the UAV to the user terminal and the average path loss, the communication link capacity between the UAV and the user terminal served by the UAV is obtained.

[0123] Specifically, it is assumed that each UAV can serve multiple user terminals, and each user terminal can only be connected to one UAV. Therefore, the horizontal distance d and the straight-line distance D between the i-th UAV and the j-th user terminal are given by the following formulas:

[0124] ;​​​​

[0125] ;

[0126] Assuming the flight height of the UAV is constant during the flight , the horizontal distance between the new deployment location of the UAV and the original deployment location is:

[0127] ;

[0128] In this embodiment, the line-of-sight and non-line-of-sight channel models are used as the path loss channel model, then the line-of-sight path loss and non-line-of-sight path loss between the UAV and the user can be expressed as:

[0129] ;

[0130] ;

[0131] wherein denotes the free space path loss, denotes the carrier frequency, denotes the free space reference distance, denotes the speed of light, denotes the line-of-sight loss parameter, denotes the non-line-of-sight loss parameter, denotes the straight-line distance between the th UAV and the th user terminal, is a Gaussian random variable representing the line-of-sight loss shadow fading, is a Gaussian random variable representing the non-line-of-sight loss shadow fading.

[0132] In addition, the line-of-sight loss probability between the th UAV and the th user terminal is:

[0133] ;

[0134] wherein and denote two environmental parameter constants, depends on the height of the building, depends on the density of the building, denotes the straight-line distance between the The first drone and the The elevation angle between user terminals can be calculated as ;

[0135] Similarly, The first drone and the The probability of non-line-of-sight loss between user terminals Therefore, the The first drone and the The average path loss between user terminals can be expressed as:

[0136] ;

[0137] In this embodiment, the downlink transmission power of each UAV is , then according to Shannon's formula, The first drone and the Communication link capacity between user terminals for:

[0138] ;

[0139] in, Indicates the The drone is assigned to The bandwidth of each user terminal, is the receiver's Gaussian white noise variance.

[0140] In some embodiments, the minimum energy consumption limit is obtained by:

[0141] Based on the UAV’s horizontal flight speed, descent speed, UAV position, and central base station position, the UAV’s first return energy consumption is obtained;

[0142] Based on the first hovering energy consumption, the movement energy 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 hovering and executing the redeployment algorithm after the power check, and the movement energy consumption is the energy consumed by the drone when moving within a preset maximum movement distance;

[0143] 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 by the drone when hovering to serve the user terminal.

[0144] In this embodiment, the first return energy consumption It is defined as the minimum power limit of the charging center that can meet the requirements of the drone returning to home and descending to the central base station. Specifically, it can be obtained by the following formula:

[0145] ;

[0146] wherein the central base station location is , the UAV location is ; denotes a horizontal flight speed of the UAV, denotes a vertical flight speed of the UAV, denotes a flight height of the UAV.

[0147] second return energy consumption refers to the UAV remaining under the premise of meeting the return, can support the UAV to wait in place for other UAVs to replace and serve the user terminal unloaded by the UAV, and the minimum energy boundary for returning to charge, specifically, can be obtained by the following formula:

[0148] ;

[0149] wherein, denotes energy consumed by hovering when the UAV performs the power check and executes the redeployment algorithm operation, denotes energy consumed by the UAV when moving under the preset maximum moving distance.

[0150] Here, , = , denotes an average duration of performing the redeployment algorithm operation after the power check of all UAVs, denotes a maximum value of the time required for all UAVs to move from the original deployment location to the new deployment location, denotes the power of the UAV.

[0151] minimum energy consumption limit refers to the power boundary that the UAV power can support to move from the original location to the new deployment point and persist in serving until the occurrence of the next low power check event, specifically, can be obtained by the following formula:

[0152] ;

[0153] wherein, denotes the second hovering energy consumption, , is a constant, representing the longest time interval between each power check.

[0154] In some embodiments, the method further comprises:

[0155] obtaining the total communication link capacity of the UAV cluster based on the communication link capacity between the UAV and the user terminal served by the UAV.

[0156] obtaining a communication rate variance of the UAV cluster based on the communication link capacity and a minimum target communication link capacity of the user terminal served by the UAV;

[0157] obtaining a total energy consumption of the UAV cluster based on a first total energy consumption of the first UAV and a second total energy consumption of the second UAV in the UAV cluster.

[0158] In this embodiment, the total communication link capacity of the UAV cluster refers to the sum of the communication link capacities between all UAVs and the user terminals served thereby, which is usually calculated based on the communication link capacity between each UAV and the user terminal served thereby. The total communication link capacity of the UAV cluster can be obtained by the following formula:

[0159] .

[0160] The communication rate variance is an index for measuring the difference in communication rate between different UAVs and user terminals in the UAV cluster. Based on the communication link capacity between each UAV and the user terminal served thereby and the minimum target communication link capacity required by the user terminal, the deviation between the communication rate provided by each UAV and the demand of the user terminal can be calculated. Then, by statistically analyzing these deviations, the communication rate variance of the UAV cluster can be obtained. Specifically, the communication rate variance of the UAV cluster can be obtained by the following formula:

[0161] ;

[0162] wherein, , .

[0163] The total energy consumption of the UAV cluster refers to the total energy consumed by all UAVs participating in the redeployment task when performing the redeployment task. Here, the calculation of the total energy consumption of the UAV cluster involves two types of UAVs: one type is the UAVs that are working before performing the current redeployment task (the first UAVs ), and the other type is the full-battery UAVs that are at the central base station before performing the current redeployment task (the second UAVs ). By calculating the total energy consumption of these two types of UAVs respectively and adding them together, the total energy consumption of the UAV cluster can be obtained.

[0164] Specifically, the first total energy consumption of the first UAVs and the second total energy consumption of the second UAVs are obtained by the following ways:

[0165] ​​a first total energy consumption of the first UAV is obtained based on the first duration for executing the redeployment algorithm after the power check, a second duration for the first UAV moving from the original deployment position to the new deployment position, and the UAV power;

[0166] a second total energy consumption of the second UAV is obtained based on a third duration for the second UAV moving from the original deployment position to the new deployment position, and the UAV power.

[0167] It should be understood that, since the first UAV is in a working state before, the first UAV will be checked for power before the redeployment algorithm is executed. Next, the first UAV will execute the redeployment algorithm, which also takes a certain amount of time. Therefore, in this embodiment, the time required for this process is referred to as the first duration. After the algorithm is executed, the first UAV needs to move from its original deployment position to a new deployment position, which takes a second duration.

[0168] In this embodiment, the first duration, the second duration, and the UAV power are combined to calculate the total energy consumption of the first UAV for completing the redeployment task, i.e., the first total energy consumption, which includes the energy consumption during the execution of the redeployment algorithm after the power check and the energy consumption during the flight.

[0169] Specifically, the first total energy consumption can be represented as wherein, N represents the number of the first UAVs, and P represents the UAV power, T1 represents the first duration, T2 represents the second duration.

[0170] Here, the first duration T1 may be the average duration for all the first UAVs to execute the redeployment algorithm after the power check, and the second duration T2 may be the maximum value of the time for all the first UAVs to move from the original deployment position to the new deployment position.

[0171] For the second UAV, since the second UAV flies to the destination from the central base station after completing the redeployment algorithm, the energy consumption calculation is mainly based on the third duration for moving from the original deployment position (i.e., the central base station) to the new deployment position. Similarly, in combination with the third duration and the UAV power, the second total energy consumption of the second UAV for completing the moving task can be calculated.

[0172] Here, the second total energy consumption can be represented as wherein, is a binary variable with a value of 0 or 1, when the value is 0, it means that the UAV has not departed, when the value is 1, it means that the UAV has departed, is the third time length.

[0173] Similarly, the third time length in the embodiment may be the maximum value of the time required for all the second unmanned aerial vehicles to move from the original deployment position to the new deployment position.

[0174] Further, the second time length corresponding to the movement of the first unmanned aerial vehicle from the original deployment position to the new deployment position and the third time length corresponding to the movement of the second unmanned aerial vehicle from the original deployment position to the new deployment position are obtained in the following manner:

[0175] based on the horizontal flight speed of the first unmanned aerial vehicle, the original deployment position of the first unmanned aerial vehicle, and the new deployment position of the first unmanned aerial vehicle, the second time length corresponding to the movement of the first unmanned aerial vehicle from the original deployment position to the new deployment position is obtained;

[0176] based on the horizontal flight speed of the second unmanned aerial vehicle, the original deployment position of the second unmanned aerial vehicle, the new deployment position of the second unmanned aerial vehicle, the vertical flight speed of the second unmanned aerial vehicle, and the flight height of the second unmanned aerial vehicle, the third time length corresponding to the movement of the second unmanned aerial vehicle from the original deployment position to the new deployment position is obtained.

[0177] Specifically, the second time length may be obtained by the following formula:

[0178] ;

[0179] the third time length may be obtained by the following formula:

[0180] ;

[0181] wherein, represents the original deployment position of the unmanned aerial vehicle, represents the new deployment position of the unmanned aerial vehicle, represents the horizontal flight speed of the unmanned aerial vehicle, represents the vertical flight speed of the unmanned aerial vehicle, represents the flight height of the unmanned aerial vehicle.

[0182] The unmanned aerial vehicle communication deployment and scheduling device for on-the-spot adjustment tasks provided by the present application is described below, and the unmanned aerial vehicle communication deployment and scheduling device for on-the-spot adjustment tasks described below can be mutually corresponding to the unmanned aerial vehicle communication deployment and scheduling method described above.

[0183] In the embodiment, the unmanned aerial vehicle communication deployment and scheduling device for on-the-spot adjustment tasks comprises:

[0184] The first unmanned aerial vehicle communication deployment and scheduling module 410 for the on-the-spot adjustment task is configured to construct a discrete event system corresponding to each unmanned aerial vehicle based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, wherein the event space includes at least one event affecting the state of the unmanned aerial vehicle, and the state space includes the state of the unmanned aerial vehicle after the occurrence of the event in the event space.

[0185] The second unmanned aerial vehicle communication deployment and scheduling module 420 for the on-the-spot adjustment task is configured to construct an unmanned aerial vehicle redeployment optimization model under a preset constraint condition, with the goal of maximizing the system utility of the unmanned aerial vehicle cluster under the current redeployment task, based on the discrete event system.

[0186] The third unmanned aerial vehicle communication deployment and scheduling module 430 for the on-the-spot adjustment task is configured to solve the unmanned aerial vehicle redeployment optimization model, obtain an optimal redeployment strategy of the unmanned aerial vehicle cluster under the current redeployment task, and schedule the unmanned aerial vehicles in the unmanned aerial vehicle cluster to execute the current redeployment task based on the optimal redeployment strategy.

[0187] 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.

[0188] The preset constraint condition includes at least one of the following:

[0189] The user terminal is served by at most one unmanned aerial vehicle in the unmanned aerial vehicle cluster.

[0190] The bandwidth allocated to the user terminal by the unmanned aerial vehicle in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit.

[0191] The total bandwidth allocated by the unmanned aerial vehicle in the unmanned aerial vehicle cluster that is in a working state is not greater than a maximum bandwidth limit.

[0192] The communication link capacity between the unmanned aerial vehicle in the unmanned aerial vehicle cluster and the user terminal served by the unmanned aerial vehicle is not less than a minimum target communication link capacity of the user terminal served by the unmanned aerial vehicle.

[0193] The first residual energy consumption of a first unmanned aerial vehicle in the unmanned aerial vehicle 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 a minimum energy consumption limit, and the second residual energy consumption of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster that is in an idle and sufficient power state after executing the current redeployment task is not less than the minimum energy consumption limit, wherein the first unmanned aerial vehicle is a working unmanned aerial vehicle before executing the current redeployment task, and the second unmanned aerial vehicle is a full-power unmanned aerial vehicle at the central base station before executing the current redeployment task.

[0194] The unmanned aerial vehicle in the unmanned aerial vehicle cluster is within a preset controllable position range.

[0195] The distance between any two UAVs in the UAV cluster is not less than a minimum distance limit.

[0196] The unmanned aerial vehicle communication deployment and scheduling device for on-the-spot adjustment task provided by the application 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, improves the response speed to sudden dynamic demand, and under the discrete event system, constructs an unmanned aerial vehicle redeployment optimization model under preset constraint conditions to maximize the system utility of the unmanned aerial vehicle cluster under the current redeployment task. The system utility comprehensively considers the total link capacity, communication rate variance, and total energy consumption of the unmanned aerial vehicle cluster, so as to ensure that the optimization of unmanned aerial vehicle deployment not only considers the communication capacity, but also considers the stability of the communication rate and the energy efficiency, thereby improving the stability and quality of the overall communication service. In addition, when solving the optimization model, by presetting constraint conditions such as limiting the bandwidth of the unmanned aerial vehicle allocated to the user terminal, ensuring that the communication link capacity meets the minimum target requirement, and ensuring that the residual 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 during task execution are ensured, the service interruption problem caused by power consumption is avoided, and the efficiency and reliability of the unmanned aerial vehicle in executing long-time tasks or complex and variable scenes are improved.

[0197] Figure 5 An example of a schematic diagram of the physical structure of an electronic device is shown in Figure 5 The electronic device can include a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other through the communications bus 540. The processor 510 can invoke the logical instructions in the memory 530 to execute the unmanned aerial vehicle communication deployment and scheduling method for on-the-spot adjustment task, which includes:

[0198] Based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, a discrete event system corresponding to each unmanned aerial vehicle is constructed, the event space includes at least one event affecting the state of the unmanned aerial vehicle, and the state space includes the state of the unmanned aerial vehicle after the occurrence of the event in the event space;

[0199] Under the discrete event system, an unmanned aerial vehicle redeployment optimization model under preset constraint conditions is constructed to maximize the system utility of the unmanned aerial vehicle cluster under the current redeployment task;

[0200] solving the unmanned aerial vehicle redeployment optimization model to obtain an optimal redeployment strategy of the unmanned aerial vehicle cluster under a current redeployment task, and scheduling unmanned aerial vehicles in the unmanned aerial vehicle cluster to execute the current redeployment task based on the optimal redeployment strategy;

[0201] The system utility is obtained based on total link capacity, communication rate variance and total energy consumption of the unmanned aerial vehicle cluster.

[0202] The preset constraint condition comprises at least one of the following:

[0203] The user terminal is served by at most one unmanned aerial vehicle in the unmanned aerial vehicle cluster;

[0204] The bandwidth allocated to the user terminal by the unmanned aerial vehicle in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit value;

[0205] The total bandwidth allocated by the unmanned aerial vehicle in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit value;

[0206] The communication link capacity between the unmanned aerial vehicle in the unmanned aerial vehicle cluster and the user terminal served by the unmanned aerial vehicle is not less than a minimum target communication link capacity of the user terminal served by the unmanned aerial vehicle;

[0207] The first residual energy of a first unmanned aerial vehicle in the unmanned aerial vehicle cluster in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than a minimum energy consumption limit value, and the second residual energy of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster in an idle and sufficient power state after executing the current redeployment task is not less than the minimum energy consumption limit value, the first unmanned aerial vehicle being an unmanned aerial vehicle working before executing the current redeployment task, and the second unmanned aerial vehicle being a full-power unmanned aerial vehicle at the central base station before executing the current redeployment task;

[0208] The unmanned aerial vehicle in the unmanned aerial vehicle cluster is in a preset controllable position range;

[0209] The distance between any two unmanned aerial vehicles in the unmanned aerial vehicle cluster is not less than a minimum distance limit value.

[0210] Further, the logic instructions in the memory 530 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the part of the prior art that contributes essentially or the part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0211] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the unmanned aerial vehicle communication deployment and scheduling method for on-site adjustment task provided by the above-mentioned method, the method comprises:

[0212] Based on the predefined event space, state space, state transition rule corresponding to the event space and the state space, a discrete event system corresponding to each unmanned aerial vehicle is constructed, at least one event affecting the state of the unmanned aerial vehicle is included in the event space, and the state space includes the state of the unmanned aerial vehicle after the occurrence of the event in the event space;

[0213] Under the discrete event system, an unmanned aerial vehicle redeployment optimization model under a preset constraint condition is constructed with the goal of maximizing the system utility of the unmanned aerial vehicle cluster under the current redeployment task;

[0214] The unmanned aerial vehicle redeployment optimization model is solved to obtain the optimal redeployment strategy of the unmanned aerial vehicle cluster under the current redeployment task, and the unmanned aerial vehicles in the unmanned aerial vehicle cluster are dispatched to execute the current redeployment task based on the optimal redeployment strategy;

[0215] Wherein, the system utility is obtained based on the total link capacity, communication rate variance and total energy consumption of the unmanned aerial vehicle cluster;

[0216] The preset constraint condition comprises at least one of the following:

[0217] The user terminal is served by at most one unmanned aerial vehicle in the unmanned aerial vehicle cluster;

[0218] The bandwidth allocated to the user terminal by the unmanned aerial vehicle in the unmanned aerial vehicle cluster is not greater than the maximum bandwidth limit value;

[0219] The total bandwidth allocated to the unmanned aerial vehicles in working state in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit value;

[0220] The communication link capacity between the unmanned aerial vehicles in the unmanned aerial vehicle cluster and the user terminals served by the unmanned aerial vehicles is not less than a minimum target communication link capacity of the user terminals served by the unmanned aerial vehicles;

[0221] The first residual energy of a first unmanned aerial vehicle in the unmanned aerial vehicle cluster in a state of waiting for allocation of a new deployment position after performing a current redeployment task is not less than a minimum energy consumption limit value, and the second residual energy of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster in an idle and sufficient power state after performing the current redeployment task is not less than the minimum energy consumption limit value, the first unmanned aerial vehicle being an unmanned aerial vehicle that was working before performing the current redeployment task, and the second unmanned aerial vehicle being a full-power unmanned aerial vehicle at the central base station before performing the current redeployment task;

[0222] The unmanned aerial vehicles in the unmanned aerial vehicle cluster are within a preset controllable position range;

[0223] The distance between any two unmanned aerial vehicles in the unmanned aerial vehicle cluster is not less than a minimum distance limit value.

[0224] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the unmanned aerial vehicle communication deployment and scheduling method for on-the-spot adjustment tasks provided by the above-mentioned methods, the method comprising:

[0225] Based on a predefined event space, a state space, and a state transition rule corresponding to the event space and the state space, a discrete event system corresponding to each unmanned aerial vehicle is constructed, the event space including at least one event affecting the state of the unmanned aerial vehicle, and the state space including the state of the unmanned aerial vehicle after the occurrence of the event in the event space;

[0226] Under the discrete event system, an unmanned aerial vehicle redeployment optimization model under a preset constraint condition is constructed with the goal of maximizing the system utility of the unmanned aerial vehicle cluster under the current redeployment task;

[0227] The unmanned aerial vehicle redeployment optimization model is solved to obtain an optimal redeployment strategy of the unmanned aerial vehicle cluster under the current redeployment task, and the unmanned aerial vehicles in the unmanned aerial vehicle cluster are dispatched to perform the current redeployment task based on the optimal redeployment strategy;

[0228] 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;

[0229] The preset constraint condition includes at least one of the following:

[0230] The user terminal is served by at most one UAV in the UAV cluster;

[0231] The bandwidth allocated to the user terminal by the UAV in the UAV cluster is not greater than the maximum bandwidth limit;

[0232] The total bandwidth allocated by the UAVs in the UAV cluster that are in the working state is not greater than the maximum bandwidth limit;

[0233] The communication link capacity between the UAV in the UAV cluster and the user terminal served by the UAV is not less than the minimum target communication link capacity of the user terminal served by the UAV;

[0234] The first residual energy of a first UAV in the UAV cluster that is in the state of waiting for allocation of a new deployment position after performing the current redeployment task is not less than the minimum energy limit, and the second residual energy of a second UAV in the UAV cluster that is in the state of being idle and having sufficient power after performing the current redeployment task is not less than the minimum energy limit, the first UAV being a UAV that was working before performing the current redeployment task, and the second UAV being a full-power UAV that was at the central base station before performing the current redeployment task;

[0235] The UAV in the UAV cluster is within a preset controllable position range;

[0236] The distance between any two UAVs in the UAV cluster is not less than the minimum distance limit.

[0237] The apparatus embodiments described above are merely illustrative, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0238] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments.

[0239] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for deployment and scheduling of UAV communication for on-the-fly adjustment task, characterized in that, The method comprises: constructing a discrete event system corresponding to each unmanned aerial vehicle based on a predefined event space, a state space, and state transition rules corresponding to the event space and the state space, the event space including at least one event affecting the state of the unmanned aerial vehicle, and the state space including the state of the unmanned aerial vehicle after the occurrence of the event in the event space; Under the discrete event system, an unmanned aerial vehicle redeployment optimization model under preset constraints is constructed with the goal of maximizing the system utility of the unmanned aerial vehicle cluster under the current redeployment task, wherein the system utility is defined as: ; represents the total link capacity of the unmanned aerial vehicle cluster, represents the communication rate variance of the unmanned aerial vehicle cluster, represents the total energy consumption of the unmanned aerial vehicle cluster, respectively represents the corresponding importance coefficient; solving the unmanned aerial vehicle redeployment optimization model to obtain an optimal redeployment strategy of the unmanned aerial vehicle cluster under the current redeployment task, and scheduling the unmanned aerial vehicles in the unmanned aerial vehicle cluster to execute the current redeployment task based on the optimal redeployment strategy; wherein 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; the preset constraint condition comprises at least one of the following: a user terminal is served by at most one unmanned aerial vehicle in the unmanned aerial vehicle cluster; the bandwidth allocated to the user terminal by the unmanned aerial vehicles in the unmanned aerial vehicle cluster is not greater than a maximum bandwidth limit value; the total bandwidth allocated by the unmanned aerial vehicles in the unmanned aerial vehicle cluster in a working state is not greater than the maximum bandwidth limit value; the communication link capacity between the unmanned aerial vehicles in the unmanned aerial vehicle cluster and the user terminals served by the unmanned aerial vehicles is not less than the minimum target communication link capacity of the user terminals served by the unmanned aerial vehicles; the first residual energy consumption of a first unmanned aerial vehicle in the unmanned aerial vehicle cluster in a state of waiting for allocation of a new deployment position after executing the current redeployment task is not less than a minimum energy consumption limit value, and the second residual energy consumption of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster in an idle and sufficient power state after executing the current redeployment task is not less than the minimum energy consumption limit value, the first unmanned aerial vehicle being a working unmanned aerial vehicle before executing the current redeployment task, and the second unmanned aerial vehicle being a full-power unmanned aerial vehicle at the central base station before executing the current redeployment task; the unmanned aerial vehicles in the unmanned aerial vehicle cluster are within a preset controllable position range; the distance between any two unmanned aerial vehicles in the unmanned aerial vehicle cluster is not less than a minimum distance limit value; wherein it further comprises: obtaining the total communication link capacity of the unmanned aerial vehicle cluster based on the communication link capacity between the unmanned aerial vehicles and the user terminals served by the unmanned aerial vehicles; obtaining the communication rate variance of the unmanned aerial vehicle cluster based on the communication link capacity and the minimum target communication link capacity of the user terminals served by the unmanned aerial vehicles; obtaining the total energy consumption of the unmanned aerial vehicle cluster based on the first total energy consumption of a first unmanned aerial vehicle and the second total energy consumption of a second unmanned aerial vehicle in the unmanned aerial vehicle cluster. 2.The method of claim 1, wherein, The communication link capacity between the unmanned aerial vehicles and the user terminals served by the unmanned aerial vehicles is obtained by: obtaining the line-of-sight loss and the non-line-of-sight loss between the unmanned aerial vehicles and the user terminals served by the unmanned aerial vehicles based on the straight-line distance therebetween; obtaining the line-of-sight loss probability between the unmanned aerial vehicles and the user terminals served by the unmanned aerial vehicles based on the horizontal distance therebetween and the flight height of the unmanned aerial vehicles; obtaining the average path loss between the unmanned aerial vehicles and the user terminals served by the unmanned aerial vehicles based on the line-of-sight loss, the non-line-of-sight loss, and the line-of-sight loss probability. Based on the bandwidth allocated to the user terminal by the UAV and the average path loss, a communication link capacity between the UAV and the user terminal served by the UAV is obtained. 3.The method of claim 1, wherein, 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: Based on a first time length for which the first UAV performs the power check and executes the re-deployment algorithm, a second time length for which the first UAV moves from the original deployment position to the new deployment position, and the UAV power, the first total energy consumption of the first UAV is obtained. Based on a third time length for which the second UAV moves from the original deployment position to the new deployment position and the UAV power, the second total energy consumption of the second UAV is obtained.

4. The method of claim 3, wherein, The second time length for which the first UAV moves from the original deployment position to the new deployment position and the third time length for which the second UAV moves 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 UAV, the original deployment position of the first UAV, and the new deployment position of the first UAV, the second time length for which the first UAV moves from the original deployment position to the new deployment position is obtained. 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 height of the second UAV, the third time length for which the second UAV moves from the original deployment position to the new deployment position is obtained.

5. The method of claim 1, wherein, The minimum energy consumption limit is obtained in the following manner: Based on the horizontal flight speed of the UAV, the descending aircraft speed, the UAV position, and the central base station position, a first return energy consumption of the UAV is obtained. Based on the first hovering energy consumption, the movement consumption, and the first return energy consumption, a second return energy consumption of the UAV is obtained, the hovering energy consumption being the energy consumed by the UAV when performing the power check and executing the re-deployment algorithm, and the movement consumption being the energy consumed by the UAV when moving within a preset maximum movement distance. Based on the second return energy consumption and a second hovering energy consumption, a minimum energy consumption limit of the UAV is obtained, the second hovering energy consumption being the energy consumed by the UAV when hovering to serve the user terminal.

6. A UAV communication deployment and scheduling device for on-the-fly 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 state transition rules corresponding to the event space and the state space, the event space including at least one event affecting the state of the UAV, and the state space including the state of the UAV after the occurrence of the event in the event space; The second unmanned aerial vehicle communication deployment and scheduling module facing the on-the-spot adjustment task is used for constructing an unmanned aerial vehicle redeployment optimization model under preset constraints with the goal of maximizing the system utility of the unmanned aerial vehicle cluster under the current redeployment task in the discrete event system. is defined as: ; represents the total link capacity of the unmanned aerial vehicle cluster, represents the communication rate variance of the unmanned aerial vehicle cluster, represents the total energy consumption of the unmanned aerial vehicle cluster, respectively represents the corresponding importance coefficient; A third UAV communication deployment and scheduling module for on-the-spot adjustment tasks, configured to solve the UAV re-deployment optimization model, obtain an optimal re-deployment strategy of the UAV cluster under the current re-deployment task, and schedule the UAVs in the UAV 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 of the UAV cluster, the communication rate variance, and the total energy consumption. The preset constraint condition includes at least one of the following: A user terminal is served by at most one UAV in the UAV cluster. The bandwidth allocated to the user terminal by the UAV in the UAV cluster is not greater than a maximum bandwidth limit value; The total bandwidth allocated to the UAV in the working state in the UAV cluster is not greater than a maximum bandwidth limit value; The communication link capacity between the UAV in the UAV cluster and the user terminal served by the UAV is not less than a minimum target communication link capacity of the user terminal served by the UAV; The first residual energy of a first UAV in the UAV cluster in a state of waiting for allocation of a new deployment position after performing a current redeployment task is not less than a minimum energy limit value, and the second residual energy of a second UAV in the UAV cluster in an idle and sufficient power state after performing the current redeployment task is not less than the minimum energy limit value, the first UAV being a UAV working before performing the current redeployment task, and the second UAV being a full-power UAV at the central base station before performing the current redeployment task; The UAV in the UAV cluster is within a preset controllable position range; The distance between any two UAVs in the UAV cluster is not less than a minimum distance limit value; The method further comprises: Obtaining the total communication link capacity of the UAV cluster based on the communication link capacity between the UAV and the user terminal served by the UAV; Obtaining the communication rate variance of the UAV cluster based on the communication link capacity and the minimum target communication link capacity of the user terminal served by the UAV; Obtaining the total energy consumption of the UAV cluster based on the first total energy consumption of the first UAV and the second total energy consumption of the second UAV in the UAV cluster.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the UAV communication deployment and scheduling method for on-the-spot adjustment tasks according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the UAV communication deployment and scheduling method for on-the-spot adjustment tasks according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the UAV communication deployment and scheduling method for on-the-spot adjustment tasks according to any one of claims 1 to 5.

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