A method for scheduling unmanned aerial vehicle crew based on a phased greedy strategy

Abstract

The application discloses a kind of unmanned aerial vehicle team scheduling methods based on stage greedy strategy, it is related to unmanned aerial vehicle technical field, including steps:T1, incoming data;T2, screening executable task;Executable task is assigned priority;T3, for a unmanned aerial vehicle team, according to executable task ability screening matching task;T4, for a unmanned aerial vehicle team, select a matching task, as the initial task within a day, the distance between the task position of initial task and the team position of current unmanned aerial vehicle team is maximum;T5, select the matching task with the minimum distance to current task and assign to current unmanned aerial vehicle team, and complete the update of preset working time;T6, repeat T5 until preset working time meets the condition;T7, repeat T3 to T6 until all unmanned aerial vehicle teams are completed scheduling. Relative to prior art, it can realize the intelligent scheduling of automation, high efficiency, low cost.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV crew scheduling method based on a phased greedy strategy. Background Technology

[0002] With the rapid development of drone technology, drones are increasingly being used in logistics, inspection, agricultural monitoring, and military applications. However, with the increasing number of drones and the growing complexity of missions, how to efficiently and intelligently allocate and schedule tasks for drones and their crews has become a key issue in optimizing the performance and efficiency of drone systems. Traditional drone scheduling methods are no longer sufficient to meet the complex demands of large-scale mission environments, while intelligent optimization methods offer an effective solution.

[0003] In existing technologies, the drone mission scheduling problem mainly involves allocating multiple drones (or drone crews) to various types of tasks while satisfying various constraints and optimizing specific objectives. Its main characteristics and challenges include:

[0004] (1) Multiple constraints: The scheduling process needs to meet multiple constraints such as task time limits, drone types, payload limits, and task priorities;

[0005] (2) High dynamism: UAV missions are usually in a dynamic environment, and mission requirements and UAV status may change in real time. The scheduling plan needs to be able to adjust quickly.

[0006] (3) Timeliness: When there are a large number of drone missions or drone crews, traditional methods based on human experience are no longer able to provide a quick response, and the program needs to quickly provide a strategy. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a drone crew scheduling method based on a phased greedy strategy. By integrating multiple constraints, the greedy strategy is used to achieve automated, efficient, and low-cost intelligent scheduling of drones / drone crews.

[0008] The technical solution of this invention is implemented as follows:

[0009] A drone crew scheduling method based on a phased greedy strategy includes the following steps:

[0010] T1, Input data; the data includes task data, crew data, and airspace data;

[0011] The airspace data includes airspace range; the mission data includes mission type, mission execution time, and mission location; the crew data includes several UAV crews, and each UAV crew includes crew location, maximum working time per day, and mission execution capability.

[0012] T2. Filter the task data whose location is within the airspace range and record them as executable tasks; assign priorities to the executable tasks; sort the executable tasks according to the priorities, with the higher the priority, the earlier the sorted task is;

[0013] T3. For a drone group, select matching task data from the executable tasks based on the executable task capability, and denot it as a matching task; wherein, the executable tasks with higher priority are matched first.

[0014] T4. For a drone group, construct a first task set; the first task set is an ordered sequence; select a matching task as the initial task for the day and add it to the first task set; wherein, the distance between the task position of the initial task and the current drone group's position is maximized; this is equivalent to the greedy strategy in the first stage. The initial task is used as the current task; initialize the preset working time of the drone group; delete the matching task that has been added to the first task set;

[0015] T5. Select the matching task with the smallest distance to the current task's location and add it to the first task set; this is equivalent to the greedy strategy in the second stage. Finding the closest task each time minimizes the distance between task sets, thus reducing losses and saving time. Calculate the path consumption time for the UAV group from the current task to the matching task;

[0016] The path consumption time and the task execution time of the matching task are added to the preset working time to complete the update of the preset working time;

[0017] The matching task is set as the new current task; the matching task that has been added to the first task set is deleted;

[0018] T6. Repeat T5 until the preset working time is greater than or equal to the maximum working time; that is, the second round of greedy strategy needs to satisfy the constraint "preset working time ≤ maximum working time".

[0019] T7. Repeat T3 to T6 until all the drone groups have completed the arrangement of the executable tasks to obtain a scheduling plan, which includes the first task set.

[0020] Airspace refers to the three-dimensional spatial area designated for aircraft flight under regulatory control and based on specific airspace classifications.

[0021] At this point, the first task set is an ordered set, that is, a set arranged in order according to the selection order of matching tasks, forming the final task scheduling scheme of a drone group.

[0022] By employing a greedy strategy, the scheduling of automated drone teams and tasks is achieved while minimizing path time losses and maximizing efficiency, meaning that the majority of the drone team's working time is devoted to executing the task itself.

[0023] In a conventional approach, whether it's the greedy selection of the initial task or the greedy selection of subsequent task nodes, choosing the nearest task for each task will result in relatively dispersed task assignments and paths. This is especially true for the last drone group to be assigned a task, where the task distribution and paths are the most dispersed, leading to high time consumption and low efficiency.

[0024] In this invention, each UAV group's initial task is selected based on the task furthest from the starting point, which allows for the relative aggregation of tasks and paths assigned to all UAV groups. Compared to existing technologies, this results in lower losses and higher efficiency. UAV groups can also complete flight missions with high efficiency.

[0025] As a further optimization of the above scheme, the task data includes a deadline; the priority includes a first level and a second level.

[0026] The first-level value is the difference between the current time and the deadline of the task data; specifically, it is the date difference. The smaller the first-level value, the higher the priority; conversely, the larger the value, the lower the priority.

[0027] When the first level is the same, the second level is compared; the value of the second level is the value converted according to the task type; at this time, the smaller the value of the second level, the higher the priority; conversely, the larger the value, the lower the priority.

[0028] This means that the first level has a higher priority than the second level.

[0029] As a further optimization of the above scheme, in T2, a weight value is generated for each task data; wherein, the values ​​of the first level and the second level are converted into integers and decimals respectively and then added together, which is the weight value; the smaller the weight value, the higher the priority; conversely, the larger the weight value, the lower the priority.

[0030] A single weight value can be used to compare two levels of values, thus simplifying the process and improving efficiency.

[0031] As a further optimization of the above scheme, the preset working time is initialized as follows: calculate the path consumption time between the task location of the initial task and the unit location, and then add it to the task execution time of the initial task, which is the preset working time.

[0032] As a further optimization of the above solution, for each of the aforementioned drone groups, the following steps are also included:

[0033] T8. Calculate the path consumption time between the unit location and the task location of the initial task;

[0034] After subtracting the path consumption time from the maximum working time, the data is then evenly distributed into multiple time periods; each time period corresponds to a second task set; each time period corresponds to a takeoff position.

[0035] The matching tasks are extracted sequentially from the first task set and populated into each of the second task sets in order, as shown below:

[0036]

[0037] Where, x i Represents the i-th first task set; a is the matching task under the i-th first task set; t a This indicates the execution time of matching task a; sT max ∑t represents the duration of each of the aforementioned time periods; a This represents the sum of the task execution times of a number of consecutive matching tasks under the i-th task set in the first task set;

[0038] The initial task is the current task of the first time period.

[0039] In practical applications, the greedy strategy greatly shortens the flight path of the drone group between multiple tasks, and the actual path consumption time can be ignored, i.e., recorded as 0.

[0040] As a further optimization of the above scheme, both the mission location and the unit location are latitude and longitude coordinates;

[0041] In step T2, it is determined whether the task data is located within the airspace based on the latitude and longitude coordinates; the distance D between the two matching tasks is calculated using the Euclidean distance method, i.e.:

[0042]

[0043] Where (a1,b1) and (a2,b2) represent the latitude and longitude coordinates of the two matching tasks, respectively.

[0044] As a further optimization of the above scheme, in step T1, the data is judged to determine its legality, resulting in legal data and illegal data; the legal data is preprocessed, and the illegal data is given a warning; the data preprocessing includes deleting invalid data and redundant data; the illegal data includes data whose values ​​exceed a preset range and data whose data type does not meet the requirements.

[0045] For example, some task data also includes the task area. The reasonable range for the task area is 0-4 square kilometers. If you enter 12, it will prompt that the task area entry is incorrect.

[0046] As a further optimization of the above scheme, if an emergency occurs during task execution according to the scheduling scheme, a secondary scheduling will be performed; the secondary scheduling includes secondary task scheduling and secondary drone scheduling.

[0047] The secondary task scheduling is as follows: when a drone group is unable to execute the next task according to the scheduling plan, a matching task that is the closest to the drone group and is not in the scheduling plan is selected based on the current drone group's location.

[0048] The secondary drone scheduling involves selecting a drone crew whose capabilities and task type match an urgent, executable task, and whose location is closest to the task location, and then dispatching it to perform the task.

[0049] As a further optimization of the above solution, the task type includes one or more of panoramic, two-dimensional, three-dimensional, video, and live streaming; the executable task capability includes one or more of panoramic, two-dimensional, three-dimensional, video, and live streaming.

[0050] In step T3, when the executable task capability of a drone group includes the task type of the executable task, the executable task is the matching task.

[0051] As a further optimization of the above solution, the task type includes one of panoramic, two-dimensional, three-dimensional, video, or live streaming; the task data of the task type corresponds to the same task execution time;

[0052] The executable task capabilities include one or more of the following: panoramic, 2D, 3D, video, and live streaming;

[0053] In step T3, when the executable task capability of a drone group includes the task type of the executable task, the executable task is the matching task.

[0054] Compared with the prior art, the present invention achieves the following beneficial effects:

[0055] (1) This invention provides a UAV crew scheduling method based on a phased greedy strategy. The phased greedy strategy automatically schedules the crews intelligently, which can simultaneously consider different task types, different crew scheduling situations under airspace restrictions, and several other constraints, and finally provide a low-cost scheduling solution.

[0056] (2) Each UAV group selects the farthest task as its initial task within a day, and then selects the closest task in turn to perform UAV flight work. This can make the tasks finally assigned to each UAV group relatively aggregated, and the time consumed by the UAV group in the path flight between tasks is less, which effectively solves the problem of overly discrete task allocation in the existing technology.

[0057] (3) The present invention can avoid the errors caused by traditional scheduling strategies, and does not require the personnel to have extensive prior knowledge of the scheduling rules and strategies for UAV missions. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating a drone crew scheduling method based on a phased greedy strategy provided in an embodiment of the present invention.

[0059] Figure 2 This is a schematic diagram of the drone group task scheduling effect provided in an embodiment of the present invention (initial task is the most recent);

[0060] Figure 3 This is a schematic diagram of the drone group task scheduling effect provided in an embodiment of the present invention (initial task is the furthest). Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] This embodiment provides a drone crew scheduling method based on a phased greedy strategy, such as... Figure 1 As shown, it includes the following steps:

[0063] T1, incoming data; the data includes task data, crew data, and airspace data.

[0064] The airspace data includes the airspace range; the task data includes the task type, task execution time, deadline, and task location; in this embodiment, the task type includes one of panoramic, two-dimensional, three-dimensional, video, or live streaming; the task execution time corresponding to the task data of the task type is the same; for example, it is assumed that the task execution time of all panoramic tasks is 0.4 hours.

[0065] The data includes several drone groups, each with its own location, maximum daily operating time, and mission capabilities. Mission capabilities include one or more of the following: panoramic, 2D, 3D, video, and live streaming.

[0066] In this embodiment, both the mission location and the crew location are latitude and longitude coordinates.

[0067] In this embodiment, the data is also judged for legality to obtain legal and illegal data; legal data is preprocessed, and illegal data is flagged; data preprocessing includes deleting invalid and redundant data; illegal data includes data whose values ​​exceed a preset range and data whose data type does not meet the requirements. For example, some task data also includes task area, and the reasonable range for task area is 0-4 square kilometers. If the input is 12, a prompt will appear indicating that the area field for the task is entered incorrectly.

[0068] Let TASK be a set of multiple task data. i If ∈TASK, then task i =

[0069] The term "kssj", "jssj", "rwlx", "hx", "mj", "qz", "jd", "wd" represents the task's start time, "jssj" represents the task's end time, "rwlx" represents the task type (panoramic, 2D, 3D, video, live stream), "hx" represents the task's flight path (only present for panoramic tasks, empty for others), "mj" represents the task's area (only present for 2D tasks, not others), "qz" represents the task's priority, and "jd" and "wd" represent the task's longitude and latitude. Each term has its own upper and lower bounds. Taking "mj" as an example, it can be formulated as: "mj"∈[L mj U mj If the input exceeds the range or the input format is illegal, a corresponding warning will be given.

[0070] T2. Filter task data whose location is within the airspace and mark them as executable tasks; specifically, determine whether the task data is within the airspace based on latitude and longitude coordinates. Assign priority to executable tasks.

[0071] In this embodiment, the priority includes a first level and a second level.

[0072] The first-level value is the difference between the current time and the deadline for a task; specifically, it is the date difference. The smaller the first-level value, the higher the priority; conversely, the larger the value, the lower the priority.

[0073] When the first level is the same, the second level is compared; the value of the second level is the value converted according to the task type; specifically, different task types are assigned values ​​of 0.1, 0.2, 0.3, etc. respectively according to priority (second level) from high to low, corresponding to panoramic, 3D, 2D, etc.

[0074] At this point, the smaller the value of the second level, the higher the priority; conversely, the larger the value, the lower the priority.

[0075] This means that the first level has a higher priority than the second level.

[0076] Specifically, a weight value is generated for each task data point. This is achieved by converting the values ​​of the first and second levels into integers and decimals respectively, then adding them together. The smaller the weight value, the higher the priority; conversely, the larger the weight value, the lower the priority. For example, a panoramic task with a deadline of 23 days has a weight of 23.1; a 3D task with a deadline of 28 days has a weight of 28.2. Since 23.1 < 28.2, the panoramic task has a higher priority than the 3D task.

[0077] A single weight value can be used to compare two levels of values, thus simplifying the process and improving efficiency.

[0078] T3. For a drone group, matching task data is selected from the executable tasks based on the executable task capability, and these are denoted as matching tasks. In this embodiment, when the executable task capability of a drone group includes a task type of an executable task, the executable task is the matching task. Among them, executable tasks with higher priority are matched first.

[0079] T4. For a drone group, construct a first task set; the first task set is an ordered sequence; select a matching task as the initial task for the day and add it to the first task set; where the distance between the initial task's location and the current drone group's location is maximized; this is equivalent to the greedy strategy in the first stage. The initial task is used as the current task; initialize the drone group's preset working time; in this embodiment, the preset working time is initialized by calculating the path consumption time between the initial task's location and the drone group's location, and then adding it to the initial task's execution time, which is the preset working time. For example, if the travel speed is 30 km / h, the time the drone group spends on the road can be calculated by the distance.

[0080] Delete the matching task that has been added to the first task set.

[0081] T5. Select the matching task with the smallest distance to the current task's location (if the distances are the same, randomly select one) and add it to the first task set; this is equivalent to the greedy strategy in the second stage. Finding the closest task each time minimizes the distance between task sets, thus reducing losses and saving time. Calculate the path consumption time of the drone group from the current task to the matching task; in this embodiment, the path consumption time of the drone group from the current task to the matching task is set to 0.

[0082] The path consumption time and the task execution time of the matching task are added to the preset working time to complete the update of the preset working time;

[0083] Set the matching task as the new current task; delete the matching task that has been added to the first task set;

[0084] T6. Repeat T5 until the preset working time is greater than or equal to the maximum working time; that is, the second round of greedy strategy needs to satisfy the constraint "preset working time ≤ maximum working time".

[0085] T7. Repeat steps T3 to T6 until all drone groups have completed their assigned tasks and a scheduling plan is obtained. The scheduling plan includes the first task set.

[0086] In this embodiment, T8 is also included, which means that the first task set of each drone group is evenly distributed to multiple time periods.

[0087] Among them, the path consumption time between the computer group location and the task location of the initial task;

[0088] After subtracting the path consumption time from the maximum working time, the data is then evenly distributed into multiple time periods; each time period corresponds to a second task set; each time period corresponds to a takeoff position.

[0089] Matching tasks are extracted sequentially from the first task set and populated into each of the second task sets in order, as shown below:

[0090]

[0091] Where, x i Let represent the i-th first task set; 'a' represents the matching task under the i-th first task set; t a This indicates the execution time of matching task a; sT max ∑t represents the duration of each time interval; a This represents the sum of the execution times of several consecutive matching tasks under the i-th first task set;

[0092] The initial task is the current task in the first time period.

[0093] In practical applications, the greedy strategy greatly shortens the flight path of the drone group between multiple tasks, and the actual path consumption time can be ignored, i.e., recorded as 0.

[0094] In the above steps, the distance D between the two matching tasks is calculated using the Euclidean distance method, that is:

[0095]

[0096] Here, (a1,b1) and (a2,b2) represent the latitude and longitude coordinates of the two matching tasks, respectively.

[0097] Airspace refers to the three-dimensional spatial area designated for aircraft flight under regulatory control and based on specific airspace classifications.

[0098] At this point, the first task set is an ordered set, that is, a set arranged in order according to the selection order of matching tasks, forming the final task scheduling scheme of a drone group.

[0099] By employing a greedy strategy, the scheduling of automated drone teams and tasks is achieved while minimizing path time losses and maximizing efficiency, meaning that the majority of the drone team's working time is devoted to executing the task itself.

[0100] Among them, such as Figure 2 As shown, following conventional thinking, whether it's the greedy selection of the initial task or the greedy selection of subsequent task nodes, choosing the nearest task for each will lead to a relatively dispersed final task allocation and path. This is especially true for the last drone group to be assigned a task, resulting in the most dispersed task distribution and path, thus causing high time consumption and low efficiency.

[0101] In this invention, each drone group's initial task is selected based on the task furthest from the starting point, which allows for the relative aggregation of the final assigned tasks and paths for all drone groups. For example... Figure 3 As shown. Compared to existing technologies, it has lower losses and higher efficiency.

[0102] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A drone crew scheduling method based on a phased greedy strategy, characterized in that, Includes the following steps: T1, Input data; the data includes task data, crew data, and airspace data; The airspace data includes airspace range; the task data includes task type, task execution time, deadline, and task location; the crew data includes several UAV crews, and each UAV crew includes its location, maximum working time per day, and task execution capability. T2. Filter the task data whose location is within the airspace range and record them as executable tasks; assign priorities to the executable tasks, the priorities including a first level and a second level; The first-level value is the difference between the current time and the deadline of the task data; the smaller the first-level value, the higher the priority; conversely, the larger the value, the lower the priority. When the first level is the same, the second level is compared; the value of the second level is the value converted according to the task type; at this time, the smaller the value of the second level, the higher the priority; conversely, the larger the value, the lower the priority. A weight value is generated for each of the task data; wherein, the values ​​of the first level and the second level are converted into integers and decimals respectively, and then added together, which is the weight value; the smaller the weight value, the higher the priority; and vice versa. The executable tasks are sorted according to their priority, with higher priority tasks appearing earlier in the list. T3. For a drone group, select matching task data from the executable tasks based on the executable task capability, and denot it as a matching task; wherein, the executable tasks with higher priority are matched first. T4. For a drone group, construct a first task set; the first task set is an ordered sequence; select a matching task as the initial task for the day and add it to the first task set; wherein the distance between the task position of the initial task and the current drone group's group position is the largest; the initial task is used as the current task; initialize the preset working time of the drone group; delete the matching task that has been added to the first task set; T5. Select the matching task with the smallest distance from the current task's location and add it to the first task set; calculate the path consumption time of the UAV group from the current task to the matching task. The path consumption time and the task execution time of the matching task are added to the preset working time to complete the update of the preset working time; The matching task is set as the new current task; the matching task that has been added to the first task set is deleted; T6. Repeat T5 until the preset working time is greater than or equal to the maximum working time; T7. Repeat T3 to T6 until all the drone groups have completed the arrangement of the executable tasks to obtain a scheduling plan, which includes the first task set.

2. The drone crew scheduling method based on a phased greedy strategy according to claim 1, characterized in that, The preset working time is initialized by calculating the path consumption time between the task location of the initial task and the unit location, and then adding it to the task execution time of the initial task, which is the preset working time.

3. The drone crew scheduling method based on a phased greedy strategy according to claim 2, characterized in that, For each of the aforementioned drone groups, the following steps are also included: T8. Calculate the path consumption time between the unit location and the task location of the initial task; After subtracting the path consumption time from the maximum working time, the data is then evenly distributed into multiple time periods; each time period corresponds to a second task set; each time period corresponds to a takeoff position. The matching tasks are extracted sequentially from the first task set and populated into each of the second task sets in order, as shown below: ； Where, x i Represents the i-th first task set; a is the matching task under the i-th first task set; t a This indicates the execution time of matching task a; sT max This indicates the duration of each of the aforementioned time periods; This represents the sum of the task execution times of a number of consecutive matching tasks under the i-th task set in the first task set; The initial task is the current task of the first time period.

4. The drone crew scheduling method based on a phased greedy strategy according to claim 1, characterized in that, Both the mission location and the unit location are latitude and longitude coordinates; In step T2, it is determined whether the task data is located within the airspace based on the latitude and longitude coordinates; the distance D between the two matching tasks is calculated using the Euclidean distance method, i.e.: ； Where (a1,b1) and (a2,b2) represent the latitude and longitude coordinates of the two matching tasks, respectively.

5. The drone crew scheduling method based on a phased greedy strategy according to claim 1, characterized in that, In step T1, the data is judged for legality to obtain legal data and illegal data; the legal data is preprocessed and the illegal data is given a warning; the data preprocessing includes deleting invalid data and redundant data; the illegal data includes data whose value exceeds a preset range and data whose data type does not meet the requirements.

6. The drone crew scheduling method based on a phased greedy strategy according to claim 1, characterized in that, When tasks are executed according to the aforementioned scheduling plan, if an emergency occurs, a secondary scheduling will be carried out; the secondary scheduling includes secondary task scheduling and secondary drone scheduling. The secondary task scheduling is as follows: when a drone group is unable to execute the next task according to the scheduling plan, a matching task that is the closest to the drone group and is not in the scheduling plan is selected based on the current drone group's location. The secondary drone scheduling involves selecting a drone crew whose capabilities and task type match an urgent, executable task, and whose location is closest to the task location, and then dispatching it to perform the task.

7. The drone crew scheduling method based on a phased greedy strategy according to claim 1, characterized in that, The task types include one or more of panoramic, 2D, 3D, video, and live streaming; the executable task capabilities include one or more of panoramic, 2D, 3D, video, and live streaming. In step T3, when the executable task capability of a drone group includes the task type of the executable task, the executable task is the matching task.

8. The drone crew scheduling method based on a phased greedy strategy according to claim 1, characterized in that, The task types include one of panoramic, 2D, 3D, video, or live streaming; the task data for each task type corresponds to the same task execution time. The executable task capabilities include one or more of the following: panoramic, 2D, 3D, video, and live streaming; In step T3, when the executable task capability of a drone group includes the task type of the executable task, the executable task is the matching task.

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