A 5G-based method, system, and storage medium for drone swarm scheduling.

Abstract

This invention discloses a 5G-based method, system, and storage medium for scheduling unmanned aerial vehicle (UAV) swarms. The UAV swarm scheduling method includes: establishing flight path tasks and setting task names, task types, and swarm types; delineating the operational area on a map and generating mapping boundaries; automatically planning executable flight paths and generating a set of flight paths based on the operational area and task type; selecting UAVs in a schedulable state from a UAV resource pool; calculating the distance from the UAV to the flight path start point and the executable range of the UAV, and performing parameter verification; generating multiple sets of UAV permutations and filtering invalid combinations to obtain a set of valid combinations; generating an initial flight path allocation scheme based on the First Fit rule, and introducing OptaPlanner for scoring and ranking, selecting the optimal scheduling result to generate a scheduling strategy; and finally, issuing scheduling commands through 5G flight control to execute the UAV swarm task.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control and swarm scheduling technology, and more specifically, to a UAV swarm scheduling method, system, and storage medium based on fifth-generation mobile communication (5G) flight control technology. Background Technology

[0002] With the continuous development of drone technology and avionics technology, drones have been widely used in various application scenarios such as surveying and modeling, urban inspection, energy facility inspection, environmental monitoring, and emergency rescue. In order to improve operational efficiency and coverage, related fields have gradually evolved from a single drone operation mode to a cluster mode of multiple drones working together, with a ground dispatch system managing and assigning tasks to multiple drones in a unified manner.

[0003] Existing drone swarm scheduling technologies are typically based on pre-defined scheduling rules or simple priority strategies, allocating routes or areas according to the number of tasks or drone IDs. While some solutions introduce basic path planning or task splitting mechanisms, they often focus on theoretical algorithm design and do not adequately consider the engineering constraints faced by drones during actual flight. For example, existing scheduling schemes often fail to fully integrate key state parameters such as the drone's real-time location, remaining battery power, and flight range, leading to scheduling results that may result in drones being unable to land or posing safety risks during actual execution.

[0004] Furthermore, at the communication level, traditional drone swarm systems mostly rely on short-range wireless communication or dedicated links for command issuance and status feedback. This results in high communication latency and limited stability, making it difficult to support real-time scheduling and dynamic adjustment of large-scale drone swarms. In scenarios where multiple drones take off, operate, and return simultaneously, existing technologies lack a unified, low-latency control and monitoring mechanism, easily leading to problems such as command delays and status asynchrony.

[0005] On the other hand, although constraint solving and intelligent scheduling technologies have been applied in production scheduling, logistics scheduling and other fields, there is still a lack of mature solutions in the existing technology that can deeply integrate such general scheduling frameworks with UAV flight control systems. In particular, when considering multiple engineering constraints such as flight safety, endurance and actual operation efficiency at the same time, there is still a lot of room for improvement in the existing UAV swarm scheduling technology.

[0006] Therefore, how to achieve efficient, reliable, and engineered cluster scheduling of multiple UAVs while ensuring flight safety and mission feasibility remains a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, and medium for scheduling unmanned aerial vehicle (UAV) swarms based on 5G flight control. By performing engineering modeling of UAV resources, flight routes, tasks, and scheduling constraints, and combining an initial allocation strategy based on First Fit with the OptaPlanner constraint solving mechanism, the invention achieves comprehensive optimization of UAV swarm scheduling results in terms of resource utilization, task efficiency, and execution security.

[0008] To achieve the above objectives, this invention provides a 5G-based drone swarm scheduling method, comprising the following steps: creating flight path tasks and configuring task parameters, including task name, task type, and swarm type; delineating the operation area on a map and generating mapping boundaries; automatically planning executable flight paths based on the operation area and task type, generating a flight path set; selecting available drones in a schedulable state from the drone resource pool; calculating the distance from the drone to the flight path start point and the executable range and performing parameter verification; generating multiple drone permutations and combinations based on the available drone set; filtering invalid drone combinations and retaining a set of valid combinations with actual executability, and the valid combinations at least meeting the endurance and mission distance requirements; generating an initial flight path allocation scheme based on the First Fit rule; introducing OptaPlanner to score and rank the initial flight path allocation scheme, the scoring process embedding a mathematical model based on the risk coefficient of 5G backhaul latency, a penalty function related to power attenuation and flight path switching times, and a dynamic safety distance cost term under multi-drone interference; selecting the optimal scheduling result based on the scoring results and generating a corresponding scheduling strategy; issuing scheduling commands through 5G flight control, based on 5G... Millisecond-level synchronization and timing are used to align cluster commands and execute drone cluster tasks; the optimal scheduling result is selected based on the scoring results and a corresponding scheduling strategy is generated; scheduling commands are issued through 5G flight control and drone cluster tasks are executed.

[0009] In some embodiments, the mission parameters further include flight altitude, heading overlap rate, lateral overlap rate, and ground resolution.

[0010] In some embodiments, the route set includes several consecutive segments and corresponding waypoint sequences, each segment including a starting waypoint, an ending waypoint, flight altitude, heading information, and flight distance.

[0011] In some embodiments, drone information in the drone resource pool is continuously reported via 5G flight control at a frequency of seconds. The information includes at least the drone's unique identifier, current latitude and longitude and altitude, remaining battery power, remaining flight range, maximum allowable flight distance, current status identifier, and the location of the takeoff point or current station.

[0012] In some embodiments, the parameter verification step includes: calculating the straight-line distance from the current position of the UAV to the starting point of the flight path based on the WGS84 geodesic algorithm; calculating the maximum flight mileage based on the remaining battery power of the UAV and the default energy consumption model per unit distance; and determining whether the maximum flight mileage of the UAV is sufficient to cover the sum of line loss and mission flight path distance. If it is insufficient, it is determined to be unusable.

[0013] In some embodiments, the rules for filtering effective drone combinations include: the total available flight mileage of drones in the combination is greater than the sum of the total mission route length and the line loss; the route distance assigned to a single drone in the combination is greater than a preset minimum threshold; and the combination size is within the current mission's limit on the number of parallel drones.

[0014] In some embodiments, the OptaPlanner scoring rules include hard constraints and soft constraints, wherein hard constraints include drone endurance, status, and distance constraints; and soft constraints include reducing the number of drones, shortening mission completion time, and reducing flight distance.

[0015] In some embodiments, the scheduling solution module is further configured with a dynamic rescheduling mechanism, which is used to dynamically adjust the OptaPlanner's scoring weights based on the remaining battery power, flight status, or communication status transmitted back in real time by the 5G flight controller when any UAV status information changes, recalculate and update the correspondence between UAVs and flight routes, generate a new optimal scheduling strategy, and issue it for execution in real time.

[0016] To achieve the aforementioned objective, this invention provides a 5G-based drone swarm scheduling system, comprising: a task management module for creating flight path tasks and configuring parameters, wherein the task parameters include task name, task type, and swarm type, and delineating the work area on an electronic map to generate a mapping boundary; and a flight path planning module, deployed on a 5G edge computing node, for automatically planning executable flight paths and generating a flight path set based on the work area and task type. This flight path planning module combines 5G base station-assisted positioning with high-precision positioning data fused with GPS / BeiDou to compensate for waypoint coordinates, and utilizes 5G during the fusion process. The base station and the drone interact with pseudorange parameters and carrier phase parameters to achieve sub-meter positioning accuracy. A drone resource management module maintains a drone resource pool and filters available drones in a schedulable state. This pool contains various heterogeneous drones with different payload capacities, endurance, and speed limits. The module matches drones with task types based on a heterogeneous constraint model and evaluates drone status using a health assessment algorithm. This algorithm uses dynamic indicators such as battery remaining capacity, 5G signal strength, and cumulative motor operating time for weighted calculations. A combination generation and verification module generates multiple drone combinations based on the available drones and calculates the distance from each drone to the flight path start point and the executable range for parameter verification. Combinations that do not meet the endurance and task distance requirements are filtered out, thus retaining a set of valid combinations with practical executability. A scheduling solution module, deployed on a 5G edge computing node, generates an initial flight path allocation scheme based on the First Fit rule for the flight path set and valid drone combinations, and introduces OptaPlanner. The initial route allocation scheme is optimized and scored; the scoring process embeds a target function model that includes a 5G backhaul delay risk coefficient, a penalty function relating power attenuation and route switching times, and a dynamic safety distance cost term under multi-aircraft interference, in order to select the optimal scheduling result and generate the corresponding scheduling strategy; the flight control command issuing module is used to issue flight and mission execution commands to the UAVs through 5G flight control according to the scheduling strategy, and to achieve millisecond-level synchronization of the cluster UAVs through the 5G network to complete command alignment, wherein enhanced mobile broadband communication slices are allocated for high-definition image transmission tasks, and ultra-reliable low-latency communication slices are allocated for flight control commands to ensure real-time collaborative execution of UAV cluster tasks.

[0017] To achieve yet another objective mentioned above, the present invention provides a calculator-readable storage medium storing a calculator program, which, when executed by a processor, implements the aforementioned 5G flight control-based drone swarm scheduling method.

[0018] The present invention has the following advantages and beneficial effects:

[0019] 1. Fully integrate 5G flight control capabilities to achieve low-latency interaction between drone status data and scheduling commands;

[0020] 2. The scheduling model is built based on real engineering constraints, and the scheduling results are more consistent with actual flight conditions;

[0021] 3. By combining First Fit and OptaPlanner, a balance can be achieved between scheduling efficiency and scheduling quality;

[0022] 4. Supports multiple scheduling strategies to adapt to different business objectives;

[0023] 5. It possesses good scalability and engineering implementation value. Attached Figure Description

[0024] Embodiments of the present invention will be described with reference to the accompanying drawings, which are briefly described below.

[0025] Figure 1 This is a schematic diagram of the UAV swarm scheduling method based on 5G flight control according to the present invention.

[0026] Figure 2 This is a schematic diagram of the UAV swarm scheduling system based on 5G flight control according to the present invention.

[0027] In the accompanying drawings, the same reference numerals generally denote the same, functionally similar and / or structurally similar components.

[0028] Figure labeling: UAV swarm scheduling system-1; task management module-10; route planning module-20; UAV resource management module-30; combination generation and verification module-40; scheduling solution module-50; flight control command issuance module-60. Detailed Implementation

[0029] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate preferred embodiments of the application. However, this application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0030] Example 1

[0031] Please see Figure 1 , Figure 1 This is a schematic diagram of the UAV swarm scheduling method based on 5G flight control according to the present invention.

[0032] The following detailed description, in conjunction with specific embodiments, illustrates a method, system, and computer-readable storage medium for drone swarm scheduling based on 5G flight control, as disclosed in this invention. Embodiment 1 uses a practically deployed drone swarm scheduling system as its application background, and provides a continuous and engineered description of the processing flow and technical logic of each stage according to the system's actual operating timeline.

[0033] In Example 1, the UAV swarm scheduling system based on 5G flight control first initiates a flight path task establishment operation through the scheduling platform. Users create new flight path tasks via the task management interface, configuring the task name, task type, and swarm type during the establishment process. The flight path type can include linear, area, or geometric flight paths to adapt to different operational objects; the swarm type is preferably an orthophoto modeling swarm, but network quality testing, gas detection, and other operational modes can also be selected according to actual needs. The task parameters further configure mapping-related parameters such as flight altitude, heading overlap rate, lateral overlap rate, and ground resolution as basic constraints for subsequent flight path planning.

[0034] After setting the task parameters, the user delineates the work area on an electronic or 3D map via the map interface. The system generates a mapping boundary based on the user-drawn polygon area and performs validity verification and standardization on the boundary coordinates. Subsequently, the route planning module automatically generates a set of executable routes covering the entire work area based on the work area boundaries, task type, and mapping parameters. The route set includes several continuous segments and corresponding waypoint sequences. Each segment includes a start waypoint, an end waypoint, flight altitude, and heading information, and the flight distance for that segment is calculated simultaneously.

[0035] After route planning is completed, the scheduling platform enters the drone resource preparation phase. The system retrieves information on all drones currently connected to the platform from the drone resource pool and, based on the real-time status data transmitted by the drones, selects a set of drones in a dispatchable state. In this embodiment, each drone corresponds to a drone resource object in the system. This object includes at least the drone's unique identifier, current latitude, longitude and altitude, remaining battery power, flight range calculated from the remaining battery power, maximum allowable flight distance, current status indicator, and the location of the takeoff point or current station. The above status information is continuously reported at a rate of seconds via 5G flight control, allowing the scheduling platform to update and dynamically maintain the drone resource status in real time.

[0036] After acquiring a set of schedulable drones, the system verifies the execution parameters and assesses the capabilities of each drone. Specifically, the system first calculates the straight-line distance from the drone's current position to the starting point of the flight path based on the WGS84 geodesic algorithm, and incorporates this distance into the line loss calculation before mission execution. Simultaneously, based on the drone's remaining battery power and a preset energy consumption model per unit distance, the system calculates the maximum flight distance the drone can perform in the current state. In some implementations, the system can also perform a safety backtracking process at the starting point of the flight path, for example, backing away by several meters in the opposite direction of the starting direction of the flight path, to avoid conflicts caused by multiple drones taking off from the same spatial location. If the assessment results show that the drone's maximum executable flight distance is insufficient to cover the sum of line loss and mission flight path distance, then the drone is deemed unusable in the current mission.

[0037] After completing the capability assessment of a single UAV, the scheduling platform enters the UAV combination generation phase. Based on the currently available UAV set, the system generates all possible UAV permutations and combinations, ranging from one UAV to multiple UAVs, according to the combination size. The combination generation process can be implemented recursively, and an index control mechanism is used to avoid the generation of duplicate combinations, thereby enumerating UAV collaborative execution schemes of different sizes as the basis for subsequent multi-strategy scheduling.

[0038] After generating the drone permutations, the system performs validity filtering on each combination. The filtering rules include, but are not limited to: whether the total available flight mileage of the drones within the combination is greater than the sum of the total mission route length and line loss; whether there are any cases where a single drone is assigned a route distance less than a preset minimum threshold; and whether the combination size exceeds the current mission's limit on the number of drones allowed in parallel. Combinations that do not meet the above conditions will be directly eliminated, retaining only the set of valid combinations that are actually executable.

[0039] After obtaining a valid set of drone combinations, the system enters the initial scheduling phase. In this embodiment, the scheduling platform uses the First Fit heuristic allocation method to initially allocate flight routes and drones. Specifically, the system traverses the list of flight route tasks or segments in a preset order, and sequentially traverses the drone resources in the drone combination for each segment. When the first drone that simultaneously meets the hard constraints such as endurance, distance, and status is detected, the segment is allocated to that drone, and the drone's remaining flight range and current position are updated in real time. The system processes subsequent segments sequentially until the initial allocation of all flight routes is completed, thus forming a complete initial scheduling scheme.

[0040] After completing the initial scheduling scheme, the system introduces a constraint-based scheduling optimization engine (such as OptaPlanner) to further optimize the scheduling results. The scheduling platform takes UAV resources, flight path tasks, and initial allocation results as input, and the allocation relationship between UAVs and flight paths as the planning entity. It also defines scheduling scoring rules, embedding a mathematical model based on a risk coefficient related to 5G backhaul latency, a penalty function relating power attenuation and flight path switching frequency, and a dynamic safety distance cost term under multi-UAV interference. Hard constraints ensure the executability of the scheduling results, including UAV endurance constraints, state constraints, and distance constraints; soft constraints optimize scheduling effectiveness, such as reducing the number of UAVs involved in scheduling, shortening the overall task completion time, and reducing the UAV's flight distance. The scheduling optimization engine searches and scores the initial scheduling scheme based on these constraints, generating multiple candidate scheduling results and ranking them according to their scores.

[0041] After obtaining the sorted list of scheduling results, the system generates corresponding scheduling strategies based on different business objectives. For example, under the resource-minimum strategy, the scheduling scheme using the fewest number of drones is prioritized; under the time-minimum strategy, the scheme with the shortest overall estimated task completion time is prioritized; and under the efficiency-maximum strategy, the scheme with the highest comprehensive score is selected. Finally, the scheduling platform selects the optimal scheduling scheme that meets the current task requirements from the above strategy results and saves the corresponding flight path allocation relationship.

[0042] After the scheduling plan is confirmed, the scheduling platform issues flight and mission execution commands to the corresponding drones via the 5G flight control module. The commands include at least the flight path sequence, takeoff order, flight parameters, and return-to-home control information. During mission execution, the drones continuously transmit their position, battery level, and speed data via the 5G network, and the scheduling platform monitors the mission execution process in real time. When the system detects that the distance between drones is below a safe threshold, the battery level is below a safe threshold, or the mission execution is abnormal, it can trigger corresponding hovering, return-to-home, or rescheduling mechanisms to ensure the safety and continuity of swarm operations.

[0043] In summary, as can be seen from the above continuous and complete description of the embodiments, the UAV swarm scheduling method based on 5G flight control disclosed in this invention fully combines actual engineering constraints and multi-level scheduling strategies, and can operate stably in real UAV swarm operation scenarios. It has good feasibility, practical value and prospects for promotion and application.

[0044] Example 2

[0045] Please see Figure 2 , Figure 2 This is a schematic diagram of the UAV swarm scheduling system based on 5G flight control according to the present invention.

[0046] like Figure 2 As shown, the present invention provides a UAV swarm scheduling system 1 based on 5G flight control, comprising: a task management module 10, a route planning module 20, a UAV resource management module 30, a combination generation and verification module 40, a scheduling solution module 50, and a flight control command issuing module 60.

[0047] First, the task management module 10 creates flight path tasks and configures task parameters, including task name, task type, and cluster type, and delineates the work area on an electronic map to generate a mapping boundary. Second, the flight path planning module 20, deployed on a 5G edge computing node, automatically plans executable flight paths and generates a flight path set based on the work area and task type. This module combines 5G base station-assisted positioning with high-precision positioning data from GPS / BeiDou fusion to compensate for waypoint coordinates. During the fusion process, pseudorange parameters and carrier phase parameters are exchanged between the 5G base station and the UAV to achieve sub-meter positioning accuracy. Third, the UAV resource management module 30 maintains a UAV resource pool and filters available UAVs in a schedulable state. The UAV resource pool contains various heterogeneous UAVs with different payload capacities, endurance, and speed limits. The UAV resource management module matches UAVs with task types based on a heterogeneous constraint model and evaluates the UAV status using a health assessment algorithm. This health assessment algorithm uses dynamic indicators such as battery remaining capacity, 5G signal strength, and cumulative motor operating time for weighted calculations.

[0048] Furthermore, the combination generation and verification module 40 is used to generate multiple drone combinations based on the available drones, and calculate the distance from each drone to the starting point of the flight path and the executable range for parameter verification, filtering out combinations that do not meet the requirements for endurance and mission distance, thereby retaining a set of valid combinations with actual executability. Additionally, the scheduling solution module 50 is deployed on a 5G edge computing node, used to generate an initial flight path allocation scheme based on the First Fit rule for the set of flight paths and valid drone combinations, and introduces OptaPlanner to optimize and score the initial flight path allocation scheme; the scoring process embeds a target function model that includes a 5G backhaul latency risk coefficient, a penalty function related to power attenuation and flight path switching times, and a dynamic safety distance cost term under multi-drone interference, in order to select the optimal scheduling result and generate a corresponding scheduling strategy. The flight control command issuing module 60 is used to issue flight and mission execution commands to the UAVs via 5G flight control according to the scheduling strategy, and to achieve millisecond-level synchronization of the clustered UAVs through the 5G network to complete command alignment. Specifically, it allocates enhanced mobile broadband communication slices for high-definition image transmission tasks and ultra-reliable low-latency communication slices for flight control commands to ensure real-time coordinated execution of UAV cluster tasks.

[0049] Example 3

[0050] The present invention provides a calculator-readable storage medium storing a calculator program, which, when executed by a processor, implements the above-mentioned 5G flight control-based drone swarm scheduling method.

[0051] While various embodiments of the invention have been described above, it should be understood that these are presented by way of example only and not as limiting. Therefore, the scope of the invention should not be limited to the above embodiments, but should be defined solely by the claims and their equivalents.

Claims

1. A method for scheduling unmanned aerial vehicle (UAV) swarms based on 5G flight control, characterized in that, Includes the following steps: Create a flight route task and configure task parameters, including task name, task type and cluster type; Delineate the work area on the map and generate the survey boundary; Automatically plan executable routes based on the work area and task type, and generate a set of routes; Select available drones that are in a schedulable state from the drone resource pool; Calculate the distance from the drone to the starting point of the flight path and the executable range, and perform parameter verification; Generate multiple drone permutations and combinations based on the available drone set; Filter out invalid drone combinations and retain the set of valid combinations that are actually feasible, and the valid combinations must at least meet the requirements of endurance and mission distance; Generate an initial route allocation scheme based on the First Fit rule; OptaPlanner is introduced to score and rank the initial route allocation scheme. The scoring process incorporates a mathematical model based on the risk coefficient of 5G backhaul latency, the penalty function related to power attenuation and the number of route switching, and the dynamic safe distance cost under multi-aircraft interference. Select the optimal scheduling result based on the scoring results and generate the corresponding scheduling strategy; By issuing scheduling commands through 5G flight control and aligning cluster commands based on 5G millisecond-level synchronization, drone cluster tasks can be executed.

2. The UAV swarm scheduling method based on 5G flight control according to claim 1, characterized in that, The mission parameters also include flight altitude, heading overlap rate, lateral overlap rate, and ground resolution.

3. The UAV swarm scheduling method based on 5G flight control according to claim 1, characterized in that: The set of routes includes several consecutive segments and corresponding waypoint sequences. Each segment includes a starting waypoint, an ending waypoint, flight altitude, heading information, and flight distance.

4. The UAV swarm scheduling method based on 5G flight control according to claim 1, characterized in that, The drone information in the drone resource pool is continuously reported via 5G flight control at a frequency of seconds. The information includes at least the drone's unique identifier, current latitude and longitude and altitude, remaining battery power, remaining flight range, maximum allowable flight distance, current status indicator, and the location of the takeoff point or current station.

5. The UAV swarm scheduling method based on 5G flight control according to claim 1, characterized in that, The parameter verification step includes: Calculate the straight-line distance from the UAV’s current position to the starting point of the flight path based on the WGS84 geodesic algorithm; Calculate the maximum flight range based on the drone's remaining battery power and the default energy consumption model per unit distance; Determine if the drone's maximum flight range is sufficient to cover the sum of line loss and mission route distance; if insufficient, it is deemed unusable.

6. The UAV swarm scheduling method based on 5G flight control according to claim 1, characterized in that, The rules for filtering effective drone combinations include: The total available flight range of the UAVs in the combination is greater than the sum of the total length of the mission route and the line loss; The flight path distance assigned to a single UAV within the combination is greater than a preset minimum threshold, which is a baseline value preset by the system based on the task type; and the combination size is within the limit range of the number of parallel UAVs in the current task.

7. The UAV swarm scheduling method based on 5G flight control according to claim 1, characterized in that, The OptaPlanner scoring rules include hard constraints and soft constraints. Hard constraints include drone endurance, status, and distance constraints; soft constraints include reducing the number of drones, shortening mission completion time, and reducing flight distance.

8. The UAV swarm scheduling method based on 5G flight control according to claim 7, characterized in that, The scheduling solution module is further configured with a dynamic rescheduling mechanism, which is used to dynamically adjust the OptaPlanner's scoring weights based on the remaining battery power, flight status or communication status transmitted back in real time by the 5G flight control system when any UAV status information changes, recalculate and update the correspondence between UAVs and flight routes, generate a new optimal scheduling strategy and issue it for execution in real time.

9. A drone swarm scheduling system based on 5G flight control, characterized in that, include: The task management module is used to create route tasks and configure the number of parameters. The task parameters include task name, task type and cluster type, and delineate the operation area on the electronic map to generate the mapping boundary. The route planning module, deployed on a 5G edge computing node, is used to automatically plan executable routes and generate a set of routes based on the work area and task type. This route planning module combines 5G base station assisted positioning with high-precision positioning data fused with GPS / BeiDou to compensate for waypoint coordinates. During the fusion process, pseudorange parameters and carrier phase parameters are exchanged between the 5G base station and the UAV to achieve sub-meter level positioning accuracy. The drone resource management module is used to maintain a drone resource pool and filter available drones in a schedulable state. The drone resource pool contains a variety of heterogeneous drones with different payload capabilities, endurance, and speed limits. The drone resource management module completes the matching of drones with task types based on a heterogeneous constraint model and evaluates the drone status through a health assessment algorithm. The health assessment algorithm performs a weighted calculation based on dynamic indicators such as battery remaining capacity, 5G signal strength, and cumulative motor operating time. The combination generation and verification module is used to generate multiple drone combinations based on the available drones, and to calculate the distance from each drone to the starting point of the flight path and the executable range for parameter verification, filtering out combinations that do not meet the requirements for endurance and mission distance, thereby retaining a set of valid combinations that are actually executable. The scheduling solution module, deployed on a 5G edge computing node, is used to generate an initial route allocation scheme based on the First Fit rule for the set of routes and the effective drone combinations. OptaPlanner is introduced to optimize and score the initial route allocation scheme. The scoring process embeds a target function model that includes a penalty function related to the 5G backhaul delay risk coefficient, power attenuation and route switching times, as well as a dynamic safety distance cost term under multi-drone interference, in order to select the optimal scheduling result and generate the corresponding scheduling strategy. The flight control command issuing module is used to issue flight and mission execution commands to the UAVs via 5G flight control according to the scheduling strategy, and to achieve millisecond-level synchronization of the cluster of UAVs through the 5G network to complete command alignment. It allocates enhanced mobile broadband communication slices for high-definition image transmission tasks and ultra-reliable low-latency communication slices for flight control commands to ensure real-time collaborative execution of UAV cluster tasks.

10. A calculator-readable storage medium, characterized in that: The calculator readable storage medium stores a calculator program, which, when executed by a processor, implements the 5G flight control-based drone swarm scheduling method as described in any one of claims 1 to 7.

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