Unmanned aerial vehicle cluster fixed-point polling task scheduling method and system in multi-task mode

Abstract

The invention discloses an unmanned aerial vehicle cluster fixed-point polling task scheduling method and system in a multi-task mode, basic parameters of an unmanned aerial vehicle cluster are initialized, and the basic parameters comprise the number of unmanned aerial vehicles and the initial position and electric quantity state of each unmanned aerial vehicle; generating a task allocation scheme of preliminary patrol based on a preset patrol point set; distributing a specific patrol point for each unmanned aerial vehicle according to the task distribution scheme; monitoring the task execution state and the residual electric quantity information of each unmanned aerial vehicle in real time; according to the task execution state and the remaining power information of each unmanned aerial vehicle, patrol task distribution of the unmanned aerial vehicles is dynamically adjusted; according to the method, the unmanned aerial vehicle cluster is subjected to initialization, task allocation, real-time monitoring and dynamic adjustment, so that efficient, reliable and intelligent allocation processing of the fixed-point polling tasks of the unmanned aerial vehicles is realized.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle inspection and scheduling, and in particular to a method and system for scheduling unmanned aerial vehicle cluster fixed-point patrol tasks in a multi-task mode. Background Art

[0002] With the rapid development of drone technology, drones (Unmanned Aerial Vehicle, UAV) are increasingly used in military, civilian, commercial and other fields.

[0003] In particular, drone swarm technology, which enables the efficient execution of complex tasks through the collaborative work of multiple drones, has become one of the research hotspots.

[0004] Drone swarms can use collective intelligence and distributed control systems to complete tasks that are difficult for a single drone to accomplish, such as large-area monitoring, reconnaissance in complex environments, and emergency rescue.

[0005] Regarding the collaborative flight control of UAV swarms, it should be noted that it is one of the core technologies for realizing multi-machine collaborative tasks.

[0006] Traditional swarm flight control systems mainly focus on UAV formation maintenance, path planning, and obstacle avoidance.

[0007] These systems are usually based on pre-set mission modes, such as formation flying, multi-aircraft collaborative mapping or fixed-point patrol, and perform task allocation and coordination through a central control unit or distributed control algorithms.

[0008] In terms of task scheduling, existing technologies mostly use static allocation or simple dynamic allocation strategies to ensure that drone clusters can efficiently cover the target area when performing tasks.

[0009] However, these methods often suffer from insufficient flexibility and low resource utilization when dealing with complex and changing task requirements and environmental conditions, making it difficult to achieve optimal task allocation.

[0010] Therefore, drone swarms perform various mission modes, such as fixed-point patrols and other tasks.

[0011] Different mission modes have significant differences in the resource requirements and mission objectives of drones. Traditional single-task scheduling methods are difficult to balance and optimize multiple tasks. Summary of the invention

[0012] The purpose of the present invention is to provide a method and system for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode, which solves the above-mentioned technical problems pointed out in the prior art.

[0013] The present invention provides a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode, comprising the following steps: Initialize the basic parameters of the drone cluster, including the number of drones, the initial position and power status of each drone; Generate a preliminary patrol task allocation plan based on the preset patrol point set; Assigning specific patrol points to each drone according to the task allocation plan; Real-time monitoring of each drone’s mission execution status and remaining power information; According to the mission execution status and remaining power information of each drone, the patrol mission allocation of drones is dynamically adjusted to ensure that all patrol points are effectively covered.

[0014] Preferably, as an implementable solution, the step of generating a preliminary patrol task allocation solution includes: Divide the preset patrol point set into different sub-areas according to the geographical location information corresponding to the patrol points; Calculate the priority of the required patrol points in each sub-area based on the coverage requirements of each sub-area; Sort each sub-area by priority and generate a preliminary task assignment list; The preliminary task assignment list is sorted according to the overall priority index of the sub-area; and a specific patrol point is assigned to each drone according to the mapping principle of first matching all patrol points ranked at the top of the entire sub-area with the drone with the highest remaining battery power.

[0015] Preferably, as an implementable implementation, the step of calculating the priority of the required patrol points in each sub-area according to the coverage requirements of each sub-area includes: Obtain and analyze the area of ​​each sub-area and the preset importance index of each patrol point within the sub-area; Then, a weight value is assigned to the patrol points within each sub-area; The weight value of each patrol point in the sub-area = (total number of patrol points in the current sub-area / unit sub-area area) × the preset importance index of the patrol point; The weight values ​​of each patrol point are summed up to determine the overall priority index of the sub-area.

[0016] Preferably, as an implementable solution, the step of dynamically adjusting the patrol task allocation of the drone includes: Detect whether the remaining power status of any drone is lower than the preset standard remaining power threshold; If the judgment is yes, the list of the current drone remaining power status ranking is updated; and the patrol mission of the low-power drone is reallocated to other drones with sufficient power; At the same time, the task lists of the transferred drones and other drones are updated to ensure the continuity and coverage of the patrol mission.

[0017] Preferably, as an implementable solution, the step of real-time monitoring of the mission execution status and remaining power information of each drone includes: Use the wireless communication module to collect the real-time location and remaining power information of each drone; Transmit the collected data to the central control unit for processing and analysis; Generate status reports for each drone for the task scheduling module to obtain.

[0018] Preferably, as an implementable embodiment, the step of generating a preliminary task assignment list further comprises: Taking the minimization of the total weight of all patrol points in the drone cluster as the goal, and presetting the first constraint and the second constraint, a multi-objective optimization algorithm is applied to obtain the optimal task allocation solution corresponding to the minimization of the total weight; Select the optimal task allocation plan and generate the final task allocation list.

[0019] Preferably, as an implementable solution, the first constraint condition is that each patrol point can only be covered by one drone; The first constraint is: ; The second constraint condition is that the total flight distance of the UAV does not exceed its remaining power.

[0020] The second constraint ; in, represents the number of patrol points, represents the number of drones, Indicates patrol point Assign to drone The weight of is a binary variable, indicating a logical point Is it assigned to a drone? , Indicates patrol point To drones The distance to the current task location, Indicates drone remaining power.

[0021] Accordingly, the present invention also provides a UAV cluster fixed-point patrol task scheduling system in a multi-task mode, comprising: A drone cluster, a central control unit, and a data storage module, wherein the drone cluster is composed of a plurality of drones, and each drone has a position detection module and a communication module; The central control unit is configured with an initialization detection module, a task scheduling module, an adjustment processing module and a status monitoring module; The initialization detection module is used to initialize the basic parameters of the drone cluster, including the number of drones, the initial position and power status of each drone; based on the preset patrol point set, generate a preliminary patrol task allocation plan; The task scheduling module is used to assign specific patrol points to each drone according to the task allocation plan; The status monitoring module is used to monitor the mission execution status and remaining power information of each drone in real time; The adjustment processing module is used to dynamically adjust the patrol task allocation of the drones according to the task execution status and remaining power information of each drone.

[0022] Preferably, as an implementable embodiment, the data storage module is used to store patrol point sets, task allocation plans and drone status data.

[0023] Preferably, as an implementable solution, the communication module is used to connect the drone cluster with the central control unit to ensure real-time transmission of data.

[0024] Preferably, as an implementable solution, the initialization detection module executes the step of generating a preliminary patrol task allocation plan, including: dividing the preset patrol point set into regions, and allocating the patrol points to different sub-regions according to the geographical location information corresponding to the patrol points; calculating the priority of the required patrol points in each sub-region according to the coverage requirements of each sub-region; sorting the sub-regions according to the priority, and generating a preliminary task allocation list; the preliminary task allocation list is sorted according to the overall priority index of the sub-region; and according to the mapping principle of giving priority to matching all the patrol points ranked at the top of the entire sub-region with the drone with the highest remaining battery power, a specific patrol point is assigned to each drone.

[0025] Preferably, as an implementable implementation; the adjustment processing module in the step of calculating the priority of the required patrol points in each sub-area based on the coverage requirements of each sub-area includes: obtaining and analyzing the area of ​​each sub-area and the preset importance index of each patrol point in the sub-area; then assigning weight values ​​to the patrol points in each sub-area; the weight value of each patrol point in the sub-area = (total number of patrol points in the current sub-area / unit sub-area area) × the preset importance index of the patrol point; summing up the sum of the weight values ​​of each patrol point to determine the overall priority index of the sub-area.

[0026] The present invention also provides a computer storage medium, the computer storage medium comprising a memory for storing a computer program; A communication interface, used to connect the memory to the processor; The processor is used to execute a computer program to implement the method for scheduling fixed-point patrol tasks of a drone cluster in the multi-task mode as described above.

[0027] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages: By analyzing the above-mentioned method and system for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode provided by the present invention, it can be known that; In specific applications, the basic parameters of the drone cluster are initialized, including the number of drones, the initial position and power status of each drone; based on the preset patrol point set, a preliminary patrol task allocation plan is generated; through the preset patrol point set and the preliminary task allocation plan, all key patrol points are ensured to be effectively covered to avoid omissions.

[0028] According to the importance of patrol points and sub-area priority index, drone resources are rationally allocated to improve overall patrol efficiency.

[0029] Assign specific patrol points to each drone according to the task allocation plan; monitor the task execution status and remaining power information of each drone in real time; According to the mission execution status and remaining power information of each drone, the patrol mission allocation of drones is dynamically adjusted to ensure that all patrol points are effectively covered.

[0030] By real-time monitoring of the drone's mission execution status and remaining power, it is possible to promptly detect and respond to abnormal conditions of the drone (such as insufficient power, mission delays, etc.), dynamically adjust task allocation, and ensure the continuity and stability of patrol missions.

[0031] The above technical solution uses a systematic approach to initialize, assign tasks, monitor in real time and dynamically adjust the drone cluster, thereby achieving efficient, reliable and intelligent allocation and processing of drone fixed-point patrol tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings are only used to illustrate the implementation methods and are not to be considered as limitations of the present invention.

[0033] Figure 1 It is a schematic diagram of the main process of a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode; Figure 2 A schematic diagram of a specific process in a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode; Figure 3 It is another specific flow chart of a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode; Figure 4 It is another specific flow chart of a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode; Figure 5 It is another specific flow chart of a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode; Figure 6 The figure is a schematic diagram of the overall architecture of a fixed-point patrol task scheduling system for a drone cluster in a multi-task mode.

[0034] Reference numerals: UAV cluster 10; location detection module 11; communication module 12; Central control unit 20 ; initialization detection module 21 ; task scheduling module 22 ; state monitoring module 23 ; adjustment processing module 24 ; data storage module 30 . DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0036] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0037] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by technicians in the field of the present invention; the terms used in the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings, and are intended to cover non-exclusive inclusions.

[0038] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0039] Embodiment 1 like Figure 1 As shown, the first embodiment of the present invention provides a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode, including the following operating steps: S10, initializing basic parameters of the drone cluster, the basic parameters including the number of drones, the initial position and power status of each drone; S20, generating a preliminary patrol task allocation plan based on a preset patrol point set; S30, assigning specific patrol points to each drone according to the task assignment plan; S40, real-time monitoring of the mission execution status and remaining power information of each drone; S50: dynamically adjust the patrol task allocation of the drones according to the task execution status and remaining power information of each drone to ensure that all patrol points are effectively covered.

[0040] In specific applications, the basic parameters of the drone cluster are initialized, including the number of drones, the initial position and power status of each drone; based on the preset patrol point set, a preliminary patrol task allocation plan is generated; through the preset patrol point set and the preliminary task allocation plan, all key patrol points are ensured to be effectively covered to avoid omissions. According to the importance of the patrol point and the sub-area priority index, drone resources are reasonably allocated to improve the overall patrol efficiency.

[0041] Then, a specific patrol point is assigned to each drone according to the task allocation plan; the task execution status and remaining power information of each drone are monitored in real time; and the patrol task allocation of the drones is dynamically adjusted according to the task execution status and remaining power information of each drone to ensure that all patrol points are effectively covered.

[0042] By real-time monitoring of the drone's mission execution status and remaining power, it is possible to promptly detect and respond to abnormal conditions of the drone (such as insufficient power, mission delays, etc.), dynamically adjust task allocation, and ensure the continuity and stability of patrol missions.

[0043] The above technical solution uses a systematic approach to initialize, assign tasks, monitor in real time and dynamically adjust the drone cluster, thereby achieving efficient, reliable and intelligent allocation and processing of drone fixed-point patrol tasks.

[0044] See also Figure 2 , preferably, as an implementable solution; the step of generating a preliminary patrol task allocation solution includes: S201, dividing the preset patrol point set into different sub-areas according to the geographical location information corresponding to the patrol points; S202, calculating the priority of patrol points required in each sub-area according to the coverage requirements of each sub-area; S203, sorting each sub-area according to priority and generating a preliminary task assignment list; S204, the preliminary task assignment list is sorted according to the overall priority index of the sub-area; and a specific patrol point is assigned to each drone according to the mapping principle of first matching all patrol points ranked at the top of the entire sub-area with the drone with the highest remaining battery power.

[0045] In the above specific technical solution, by dividing the preset patrol points into areas and sorting them according to the priorities of the sub-areas, it is ensured that important areas are covered by patrol resources first.

[0046] The drones with the highest remaining battery power are prioritized to establish a mapping relationship with the top-ranked sub-areas, ensuring that high-priority areas are given priority, while taking into account drones with sufficient battery power to ensure the comprehensiveness and coverage of the patrol mission.

[0047] See also Figure 3 , preferably, as an implementable embodiment; the step of calculating the priority of the required patrol points in each sub-area according to the coverage requirements of each sub-area includes: S2021. Obtain and analyze the area of ​​each sub-area and the preset importance index of each patrol point in the sub-area; S2022, then assigning a weight value to each patrol point in each sub-area; The weight value of each patrol point in the sub-area = (total number of patrol points in the current sub-area / unit sub-area area) × the preset importance index of the patrol point; S2023. Summarize the total weight values ​​of each patrol point to determine the overall priority index of the sub-area.

[0048] See also Figure 4 Preferably, as an implementable embodiment, the step of dynamically adjusting the patrol task allocation of the drone includes: S501, detecting whether the remaining power state of any UAV is lower than a preset standard remaining power threshold; S502: If the determination is yes, then update the list of rankings of the remaining battery status of the current drones; and reallocate the patrol missions of the drones with low battery to other drones with sufficient battery; S503, simultaneously update the task list of the transferred task drone and other drones to ensure the continuity and coverage of the patrol task. The above technical solution can monitor the remaining power of the drone in real time, reasonably arrange the charging and working time, and avoid task interruption due to insufficient power.

[0049] See also Figure 5 Preferably, as an implementable embodiment, the step of real-time monitoring of the task execution status and remaining power information of each drone includes: S401, using the wireless communication module to collect the real-time location and remaining power information of each drone; S402, transmitting the collected data to a central control unit for processing and analysis; S403: Generate a status report of each UAV for the task scheduling module to obtain.

[0050] In the technical solution of the present invention, the wireless communication module is used to obtain the location and power information of the drone in real time to ensure the timeliness and accuracy of the data. The central control unit processes the collected data for analysis and generates a status report to provide reliable data support for task scheduling. The task scheduling module can timely grasp the operating status of the drone, respond and adjust quickly, and improve the overall efficiency and flexibility of the patrol system.

[0051] Preferably, as an implementable embodiment, the step of generating a preliminary task assignment list further comprises: Taking the minimization of the total weight of all patrol points in the drone cluster as the goal, and presetting the first constraint and the second constraint, a multi-objective optimization algorithm is applied to obtain the optimal task allocation solution corresponding to the minimization of the total weight; Select the optimal task allocation plan and generate the final task allocation list.

[0052] Preferably, as an implementable solution, the first constraint condition is that each patrol point can only be covered by one drone; The first constraint is: ; The second constraint condition is that the total flight distance of the UAV does not exceed its remaining power.

[0053] The second constraint ; in, represents the number of patrol points, represents the number of drones, Indicates patrol point Assign to drone The weight of is a binary variable, indicating a logical point Is it assigned to a drone? , Indicates patrol point To drones The distance to the current task location, Indicates drone remaining power.

[0054] By applying a multi-objective optimization algorithm and comprehensively considering factors such as patrol point weight, drone distance, and power, we can achieve the optimal solution for task allocation and improve resource utilization. That is, with the goal of minimizing the total weight, combined with constraints, the multi-objective optimization algorithm is used to obtain the optimal task allocation solution to ensure the optimality of resource allocation.

[0055] Embodiment 2 Based on the same concept of the above-mentioned method embodiment, the embodiment of the present invention also provides a drone cluster fixed-point patrol task scheduling system in a multi-task mode, which is used to implement the above-mentioned method of the present invention. Since the principles and methods of solving the problems in the system embodiment are similar, it has at least all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be repeated here one by one.

[0056] See also Figure 6 The present invention provides a UAV cluster fixed-point patrol task scheduling system in a multi-task mode, comprising: A drone cluster 10, a central control unit 20, and a data storage module 30. The drone cluster is composed of a plurality of drones, each of which has a position detection module 11 (for obtaining GPS position information of the current drone) and a communication module 12; The central control unit 20 is configured with an initialization detection module 21, a task scheduling module 22, a state monitoring module 23 and an adjustment processing module 24; The initialization detection module 21 is used to initialize the basic parameters of the drone cluster, including the number of drones, the initial position and power status of each drone; based on the preset patrol point set, generate a preliminary patrol task allocation plan; The task scheduling module 22 is used to assign specific patrol points to each drone according to the task allocation plan; The state monitoring module 23 is used to monitor the mission execution status and remaining power information of each drone in real time; The adjustment processing module 24 is used to dynamically adjust the patrol task allocation of the drones according to the task execution status and remaining power information of each drone.

[0057] The data storage module 30 is used to store patrol point sets, task allocation plans and drone status data; the communication module 12 is used to connect the drone cluster and the central control unit to ensure real-time data transmission.

[0058] Preferably, as an implementable solution, the initialization detection module 11 performs the step of generating a preliminary patrol task allocation solution, including: Divide the preset patrol point set into different sub-areas according to the geographical location information corresponding to the patrol points; calculate the priority of the required patrol points in each sub-area according to the coverage requirements of each sub-area; Sort each sub-region according to priority and generate a preliminary task assignment list; the preliminary task assignment list is sorted according to the overall priority index of the sub-region; And according to the mapping principle of giving priority to the drone with the highest remaining battery power, all patrol points ranked at the top of the entire sub-area are matched, and specific patrol points are assigned to each drone.

[0059] Preferably, as an implementable embodiment, the adjustment processing module 24 calculates the priority of the patrol points required in each sub-area according to the coverage requirements of each sub-area, including: Obtain and analyze the area of ​​each sub-area and the preset importance index of each patrol point within the sub-area; Then, a weight value is assigned to each patrol point in each sub-area; the weight value of each patrol point in the sub-area = (total number of patrol points in the current sub-area / unit sub-area area) × the preset importance index of the patrol point; The weight values ​​of each patrol point are summed up to determine the overall priority index of the sub-area.

[0060] Embodiment 3 On the other hand, this third embodiment is based on the method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode provided in the first embodiment of the invention, and also provides a computer storage medium (referred to as storage medium).

[0061] The computer storage medium includes a memory for storing a computer program; A communication interface, used to connect the memory to the processor; A processor is used to execute a computer program to implement an embodiment disclosed in combination with any of the above-mentioned implementation methods, involving a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode.

[0062] In summary, the method and system for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode proposed in the example of the present invention, in specific application, initializes the basic parameters of the drone cluster, the basic parameters include the number of drones, the initial position and power status of each drone; generates a preliminary patrol task allocation plan based on a preset patrol point set; through the preset patrol point set and the preliminary task allocation plan, ensure that all key patrol points are effectively covered to avoid omissions. According to the importance of the patrol point and the sub-region priority index, reasonably allocate drone resources to improve the overall patrol efficiency. According to the task allocation plan, allocate specific patrol points to each drone; monitor the task execution status and remaining power information of each drone in real time; according to the task execution status and remaining power information of each drone, dynamically adjust the patrol task allocation of the drone to ensure that all patrol points are effectively covered. By monitoring the task execution status and remaining power of the drone in real time, it is possible to timely discover and respond to abnormal conditions of the drone (such as insufficient power, task delay, etc.), dynamically adjust the task allocation, and ensure the continuity and stability of the patrol task.

[0063] The above technical solution uses a systematic approach to initialize, assign tasks, monitor in real time and dynamically adjust the drone cluster, thereby achieving efficient, reliable and intelligent allocation and processing of drone fixed-point patrol tasks.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. A person skilled in the art may modify the technical solutions described in the above embodiments, or replace part or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode, characterized in that: The steps are as follows: Initialize the basic parameters of the drone cluster, including the number of drones, the initial position and power status of each drone; Generate a preliminary patrol task allocation plan based on the preset patrol point set; Assigning specific patrol points to each drone according to the task allocation plan; Real-time monitoring of each drone’s mission execution status and remaining power information; According to the mission execution status and remaining power information of each drone, the patrol mission allocation of drones is dynamically adjusted.

2. The method according to claim 1, characterized in that The step of generating a preliminary patrol task allocation plan includes: Divide the preset patrol point set into different sub-areas according to the geographical location information corresponding to the patrol points; Calculate the priority of the required patrol points in each sub-area based on the coverage requirements of each sub-area; Sort each sub-area by priority and generate a preliminary task assignment list; The preliminary task assignment list is sorted according to the overall priority index of the sub-area; and a specific patrol point is assigned to each drone according to the mapping principle of first matching all patrol points ranked top in the entire sub-area with the drone with the highest remaining battery power.

3. The method according to claim 1, characterized in that The step of calculating the priority of patrol points required in each sub-area according to the coverage requirements of each sub-area includes: Obtain and analyze the area of ​​each sub-area and the preset importance index of each patrol point within the sub-area; Then, a weight value is assigned to the patrol points within each sub-area; The weight value of each patrol point in the sub-area = (total number of patrol points in the current sub-area / unit sub-area area) × the preset importance index of the patrol point; The weight values ​​of each patrol point are summed up to determine the overall priority index of the sub-area.

4. The method according to claim 3, characterized in that The step of dynamically adjusting the patrol task allocation of the UAV includes: Detect whether the remaining power status of any drone is lower than the preset standard remaining power threshold; If the judgment is yes, the list of the current drone remaining power status ranking is updated; and the patrol mission of the low-power drone is reallocated to other drones with sufficient power; At the same time, the task lists of the transferred mission drones and other drones are updated.

5. The method according to claim 1, characterized in that The step of real-time monitoring of the mission execution status and remaining power information of each drone includes: Use the wireless communication module to collect the real-time location and remaining power information of each drone; Transmit the collected data to the central control unit for processing and analysis; Generate status reports for each drone for the task scheduling module to obtain.

6. The method according to claim 1, characterized in that The step of generating a preliminary task assignment list further comprises: Taking the minimization of the total weight of all patrol points in the drone cluster as the goal, and presetting the first constraint and the second constraint, a multi-objective optimization algorithm is applied to obtain the optimal task allocation solution corresponding to the minimization of the total weight; Select the optimal task allocation plan and generate the final task allocation list.

7. The method according to claim 6, characterized in that The first constraint condition is that each patrol point can only be covered by one drone; The first constraint is: ; The second constraint condition is that the total flight distance of the drone does not exceed its remaining power; The second constraint ; in, represents the number of patrol points, represents the number of drones, Indicates patrol point Assign to drone The weight of is a binary variable, indicating a logical point Is it assigned to a drone? , Indicates patrol point To drones The distance to the current task location, Indicates drone remaining power.

8. A UAV cluster fixed-point patrol task scheduling system in multi-task mode, characterized in that: The system includes: A drone cluster, a central control unit, and a data storage module, wherein the drone cluster is composed of a plurality of drones, and each drone has a position detection module and a communication module; The central control unit is configured with an initialization detection module, a task scheduling module, an adjustment processing module and a status monitoring module; The initialization detection module is used to initialize the basic parameters of the drone cluster, including the number of drones, the initial position and power status of each drone; based on the preset patrol point set, generate a preliminary patrol task allocation plan; The task scheduling module is used to assign specific patrol points to each drone according to the task allocation plan; The status monitoring module is used to monitor the mission execution status and remaining power information of each drone in real time; The adjustment processing module is used to dynamically adjust the patrol task allocation of the drones according to the task execution status and remaining power information of each drone.

9. The UAV cluster fixed-point patrol task scheduling system in a multi-task mode according to claim 8 is characterized in that: The data storage module is used to store patrol point sets, task allocation plans and drone status data; the communication module is used to connect the drone cluster and the central control unit.

10. A computer storage medium, the computer storage medium comprising a memory for storing a computer program; A communication interface, used to connect the memory to the processor; A processor is used to execute a computer program to implement a method for scheduling fixed-point patrol tasks of a drone cluster in a multi-task mode as described in any one of claims 1 to 7 above.

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