CN117933669B - Dynamic task allocation method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a dynamic task allocation method, a dynamic task allocation device, computer equipment and a storage medium. The method comprises the following steps: when a new task is detected, carrying out consistency policy-based allocation processing on the current task according to task priority information in task information and a priority order from high to low, wherein the allocation processing comprises that each unmanned platform calculates bidding price information for executing the current task, and all unmanned platforms agree on allocation of the current task according to task type information of the current task and bidding price information of the current task; and obtaining task reassignment scheme information until the dynamic assignment of all newly added tasks is completed. The method can effectively reduce the communication traffic required by the unmanned platform team to achieve new plan agreement, improves the real-time performance of the method, can solve the problem of dynamic task allocation of a plurality of unmanned platform tasks to cooperatively execute a plurality of new tasks, and has better practicability.
Description
Technical Field
The application relates to the technical field of intelligent unmanned platform cooperative control, in particular to a dynamic task allocation method, a dynamic task allocation device, computer equipment and a storage medium.
Background
Along with the development of science and technology, intelligent unmanned equipment is widely applied to various industries. On the basis of the known task sequence, after the task is distributed by utilizing a plurality of task pre-distribution methods, each unmanned platform starts to execute the task according to the task execution plan. Due to the existence of uncertainty factors, tasks tend to dynamically appear in the task execution process, new tasks may threaten the unmanned platform, and the new tasks need to be rapidly distributed to the unmanned platform under the condition of meeting the task time window requirements, the differential constraint of the environment where the air-ground platform is located and the resource capacity matching requirements. The continuous multiple tasks increase the burden of the unmanned platform, and for the unmanned platform with excessive tasks, the unmanned platform is easy to end due to insufficient endurance or excessive load tasks.
Aiming at the newly appeared tasks, how to rapidly distribute the tasks to the unmanned platforms and effectively balance the task loads of the unmanned platforms is a problem to be solved in the dynamic task distribution. The prior art cannot deal with the situation that a plurality of newly-added tasks are cooperatively distributed by a plurality of unmanned platform tasks, and has the problem of poor adaptability.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a dynamic task allocation method, apparatus, computer device, and storage medium based on a segment consistency policy, which can implement task collaborative allocation of multiple unmanned platforms to multiple newly added tasks.
A method of dynamic task allocation, the method comprising:
Acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform;
When a new task is detected, task information is acquired; the task information comprises the number of newly added tasks, task type information of each newly added task and task priority information;
According to the task priority information, carrying out consistency policy-based allocation processing on the current task according to the order of the priority from high to low; the allocation process based on the consistency policy comprises the following steps: calculating bid and offer information of executing the current task by each unmanned platform, and achieving consensus by all unmanned platforms according to task type information of the current task and allocation of the bid and offer information of the current task to the current task by all unmanned platforms;
and when the total number of the reassigned tasks reaches the number of the newly-added tasks, completing the dynamic assignment of all the newly-added tasks to obtain task reassignment scheme information.
In one embodiment, the method further comprises: obtaining a task execution path of each unmanned platform according to the task pre-allocation scheme information;
inserting the current task into the middle of two adjacent tasks in the task execution path of the current unmanned platform in sequence, and calculating the income generated by executing the current task when the current task is inserted into each position;
and comparing the benefits at all possible insertion positions, setting the maximum of the benefits as a bidding price, and obtaining bidding price information of the current unmanned platform for executing the current task.
In one embodiment, the method further comprises: and sequentially inserting the current task into the middle of two adjacent tasks in the task execution path of the current unmanned platform so as to calculate the income generated by the earliest execution time when the current task is executed at the current position.
In one embodiment, the method further comprises: judging whether the capabilities of the current unmanned platform are matched;
Judging whether a road between the current task and the task before and after the insertion point is communicated or not;
judging whether the current task is inserted into the current position to meet the time window requirement or not;
If any of the above is not satisfied, the benefit of inserting the current task at the current location is set to 0.
In one embodiment, the method further comprises: determining the earliest time when the current unmanned platform performs the previous task at the current insertion position and can start to perform the insertion task;
determining a latest time to execute the insertion task at the current insertion location without affecting a next task;
and if the earliest time is earlier than the latest time, judging that the current task is inserted into the current position to meet the time window requirement.
In one embodiment, the method further comprises: determining task demand information according to task type information of a current task; the task demand information comprises needed heterogeneous unmanned platform type information and corresponding quantity information;
Bidding the current task by the unmanned platform in a bidding auction stage to obtain bidding information;
In the consistency stage, each unmanned platform and other unmanned platforms which can be reached by communication of the unmanned platform mutually transmit bidding information which bidding tasks are considered by the unmanned platform to be executed according to the bidding information, and the unmanned platform with the highest bidding corresponding to the number is selected according to the bidding information and the task demand information;
consensus is reached on the allocation of current tasks by alternating iterations of the bidding auction phase and the uniformity phase.
In one embodiment, the method further comprises: the heterogeneous unmanned platform comprises an unmanned aerial vehicle and an unmanned aerial vehicle.
A dynamic task allocation apparatus, the apparatus comprising:
The task pre-allocation module is used for acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform;
The task information acquisition module is used for acquiring task information when detecting a new task; the task information comprises the number of newly added tasks, task type information of each newly added task and task priority information;
The dynamic allocation module is used for carrying out allocation processing based on a consistency policy on the current task according to the task priority information and the order of the priorities from high to low; the allocation process based on the consistency policy comprises the following steps: calculating bid and offer information of executing the current task by each unmanned platform, and achieving consensus by all unmanned platforms according to task type information of the current task and allocation of the bid and offer information of the current task to the current task by all unmanned platforms;
And the output module is used for completing the dynamic allocation of all the newly-added tasks when the total number of the reallocated tasks reaches the number of the newly-added tasks, and obtaining task reallocation scheme information.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform;
When a new task is detected, task information is acquired; the task information comprises the number of newly added tasks, task type information of each newly added task and task priority information;
According to the task priority information, carrying out consistency policy-based allocation processing on the current task according to the order of the priority from high to low; the allocation process based on the consistency policy comprises the following steps: calculating bid and offer information of executing the current task by each unmanned platform, and achieving consensus by all unmanned platforms according to task type information of the current task and allocation of the bid and offer information of the current task to the current task by all unmanned platforms;
and when the total number of the reassigned tasks reaches the number of the newly-added tasks, completing the dynamic assignment of all the newly-added tasks to obtain task reassignment scheme information.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform;
When a new task is detected, task information is acquired; the task information comprises the number of newly added tasks, task type information of each newly added task and task priority information;
According to the task priority information, carrying out consistency policy-based allocation processing on the current task according to the order of the priority from high to low; the allocation process based on the consistency policy comprises the following steps: calculating bid and offer information of executing the current task by each unmanned platform, and achieving consensus by all unmanned platforms according to task type information of the current task and allocation of the bid and offer information of the current task to the current task by all unmanned platforms;
and when the total number of the reassigned tasks reaches the number of the newly-added tasks, completing the dynamic assignment of all the newly-added tasks to obtain task reassignment scheme information.
According to the dynamic task allocation method, the dynamic task allocation device, the computer equipment and the storage medium, task information is acquired when a new task is detected, allocation processing based on a consistency policy is carried out on a current task according to task priority information in the task information and the order of priority from high to low, the allocation processing comprises the steps that bidding bid information of executing the current task is calculated by each unmanned platform, and consensus is achieved on allocation of the current task by all unmanned platforms according to task type information of the current task and bidding bid information of the current task by all unmanned platforms; and obtaining task reassignment scheme information until the dynamic assignment of all newly added tasks is completed. The invention can effectively reduce the communication traffic required by the unmanned platform team to achieve new plan agreement, improves the real-time performance of task reassignment, can solve the problem of dynamic task assignment of a plurality of unmanned platform tasks to cooperatively execute a plurality of newly-added tasks, and has better practicability.
Drawings
FIG. 1 is a flow diagram of a dynamic task allocation method in one embodiment;
FIG. 2 is a schematic diagram of inserting tasks in a task path of an unmanned platform in one embodiment;
FIG. 3 is a flow diagram of a dynamic task allocation method based on a segment consistency policy in one embodiment;
FIG. 4 is a block diagram of a dynamic task allocation device in one embodiment;
Fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, a dynamic task allocation method is provided, including the following steps:
step 102, acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform.
The input tasks to be executed comprise reconnaissance and striking tasks, and the heterogeneous unmanned platforms capable of executing tasks comprise unmanned aerial vehicles and unmanned vehicles with reconnaissance and striking capabilities. Assuming that the unmanned aerial vehicle can directly arrive between any two task points, the road connectivity of the unmanned aerial vehicle between the two task points needs to be configured when input.
The embodiment calls a distributed task pre-allocation method based on an improved contract net to generate a task allocation scheme. The task allocation scheme includes which unmanned platforms each task is executed by, and a task execution path (task execution time plan) in which the unmanned platforms execute the task.
And 104, acquiring task information when the newly added task is detected.
The task information includes the number of newly added tasks, task type information of each newly added task, and task priority information.
When the newly added task is detected, each unmanned platform establishes communication connection with other platforms. The communication topology may also change due to the distance between the unmanned platforms changing during the execution of the task, but each two unmanned platforms may still be reachable by direct or indirect communication.
The task types comprise reconnaissance or striking tasks, and a plurality of unmanned aerial vehicles and unmanned aerial vehicles of corresponding types are respectively required to form a combination.
By only reassigning tasks which are not completed at the current moment, the communication traffic required by an unmanned platform team for achieving new plan consistency is effectively reduced, and the real-time performance of task reassignment is improved.
And 106, according to the task priority information, carrying out allocation processing based on the consistency policy on the current task according to the order of the priorities from high to low.
The allocation process based on the consistency policy includes: and calculating the bidding price information of the current task by each unmanned platform, and achieving consensus by all unmanned platforms according to the task type information of the current task and the allocation of the bidding price information of the current task to the current task by all unmanned platforms.
Specifically, for newly added tasksThe bid price calculation method of (2) is as follows: unmanned platform/>The current execution task path is/>The task currently requiring consensus to be reached is/>Execution task/>The number of unmanned aerial vehicles and unmanned aerial vehicles required is/>, respectivelyAnd/>。/>Task/>, in turnInsert into its path list/>In the middle of two adjacent tasks, executing tasks/>, when computing and inserting the tasks into each positionGenerated benefits, comparing benefits at all possible insertion locations, setting the maximum of benefits as unmanned platform/>For task/>Bid offer/>. This embodiment is shown in FIG. 2 at/>Task/>And/>A schematic of the task is inserted.
At the time of obtaining the pair taskBid of/>Thereafter, the unmanned platform begins to perform tasks/>The assignment result of (2) is agreed, and the process of agreement is divided into two stages: a bidding auction phase and a consistency phase.
In the bidding auction phase, the unmanned platform bids for the task. As long as its bid value is greater than 0 and greater than the minimum of the maximum bids that the unmanned platform deems at the current time.
The unmanned platform in the consistency stage and other unmanned platforms with reachable communication mutually transmit corresponding bidding information which bidding tasks considered by the unmanned platform are supposed to be executed by the unmanned platform, and when the information is received, the unmanned platform selects the unmanned platform with the highest bid (the number is the number of unmanned platforms needed for executing the collaborative tasks) from all the received bidding information.
And step 108, when the total number of the reassigned tasks reaches the number of the newly added tasks, completing the dynamic assignment of all the newly added tasks, and obtaining task reassignment scheme information.
In the dynamic task allocation method, task information is acquired when a new task is detected, allocation processing based on a consistency policy is carried out on a current task according to task priority information in the task information and a priority order from high to low, wherein the allocation processing comprises the steps that each unmanned platform calculates and executes bidding price information of the current task, and all unmanned platforms agree with the allocation of the current task according to the task type information of the current task and the bidding price information of the current task; and obtaining task reassignment scheme information until the dynamic assignment of all newly added tasks is completed. The invention can effectively reduce the communication traffic required by the unmanned platform team to achieve new plan agreement, improves the real-time performance of task reassignment, can solve the problem of dynamic task assignment of a plurality of unmanned platform tasks to cooperatively execute a plurality of newly-added tasks, and has better practicability.
In one embodiment, the method further comprises: and sequentially inserting the current task into the middle of two adjacent tasks in the task execution path of the current unmanned platform so as to calculate the generated benefits of the earliest execution time when the current task is executed at the current position. Judging whether the capabilities of the current unmanned platform are matched; judging whether a road between the current task and the task before and after the insertion point is communicated or not; judging whether the current task is inserted into the current position to meet the time window requirement or not; if any of the above is not satisfied, the benefit of inserting the current task at the current location is set to 0.
The benefit is defined as a function of the earliest execution time, as long as the function satisfies the task start time, the earlier the benefit is.
In calculating the benefit at each insertion location, three decision mechanisms need to be adopted to ensure the insertion taskRear/>The task execution time in (a) is not affected.
(1) The task type matches the capabilities of the unmanned platform. TasksIs classified into a reconnaissance or a hit task, and requires several corresponding types of unmanned aerial vehicles and unmanned vehicles, thus/>It is necessary to check whether the capabilities of itself match the task requirements. This correspondence is denoted as/>The following steps are:
；
（2） And judging the connectivity of the road between the task before and after the insertion point. If/> For unmanned aerial vehicle, consider unmanned aerial vehicle can be arrived directly between arbitrary two task points, need not to judge connectivity. If/>If the vehicle is an unmanned vehicle, judgment of/>Road connectivity between the two task points. Determination of/>, using Dijkstra's algorithmConnectivity thereto, where an insertion point is considered valid only if the insertion point is fully in communication with the road between the front and back tasks. Record/>Connectivity judgment function with other task points is/>，
Then there are:
；
representing the task/>, obtained by Dijkstra algorithm And unmanned platform/>The shortest path length between the mth tasks of (c).
(3) And judging a time window. Unmanned platformAfter the mth task is executed, task/>The earliest start execution time of (2) is:
；
Wherein, Representation/>Time to begin executing task mth task,/>Representing the duration of the mth task. Unmanned platform starts executing task/>The latest time requirement of the nth task must not affect the start execution time of the nth task, note/>For/>Start execution/>The latest time is:
；
Wherein, Representation of unmanned platform/>A start time at which execution of the nth task is started. Therefore, if the insertion task/>The execution time of the front and rear tasks is not influenced after that, and the task/>Is performed within a prescribed time window, should:。
when the unmanned platform calculates the task income at the insertion point, the unmanned platform firstly performs the capability matching, road connectivity judgment and time window judgment, if the capability matching, the road connectivity judgment and the time window judgment cannot be completely satisfied, the bidding bid is 0, otherwise, the task is calculated according to the fastest arrival time Is a benefit of the execution of (a).
By introducing various judging mechanisms, the method effectively adapts to complex space-time cooperative constraint in the task allocation model of the heterogeneous unmanned platform.
In one embodiment, the method further comprises: determining task demand information according to task type information of a current task; the task demand information comprises needed heterogeneous unmanned platform type information and corresponding quantity information; bidding the current task by the unmanned platform in a bidding auction stage to obtain bidding information; in the consistency stage, each unmanned platform and other unmanned platforms with reachable communication mutually transmit bidding information which bidding tasks are considered by the unmanned platforms to be executed according to the bidding information, and the unmanned platform with the highest bidding corresponding to the number is selected according to the bidding information and task demand information; consensus is reached on the allocation of current tasks by alternating iterations of the bidding auction phase and the uniformity phase.
By alternating the iteration of the bidding auction phase and the consistency phase, the unmanned platform can achieve consistency of the allocation results after a limited number of iterations.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 3, a dynamic task allocation method based on a segment consistency policy is provided, including:
Step 302: the unmanned platform performs priority ranking on the newly added tasks;
In order to enable different unmanned platforms to determine the same task consensus sequence, tasks are prioritized according to the value of the tasks, and tasks with high task values are preferentially allocated.
Step 304: judging whether a task which does not reach consensus exists, if not, ending the segmentation consistency algorithm, otherwise executing step 306;
Step 306: setting the tasks which have highest priority and do not reach consensus as tasks which need to reach consensus;
Step 308: each platform agrees with the task to be agreed according to the current execution task path of each platform;
step 310: the unmanned platform updates the execution task path and continues to step 304.
In one embodiment, as shown in fig. 4, there is provided a dynamic task allocation device, including: a task pre-allocation module 402, a task information acquisition module 404, a dynamic allocation module 406, and an output module 408, wherein:
The task pre-allocation module 402 is configured to obtain a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generate task pre-allocation scheme information through a pre-allocation algorithm, and execute the tasks by the heterogeneous unmanned platform;
The task information acquisition module 404 is configured to acquire task information when detecting a new task; the task information comprises the number of the newly added tasks, task type information of each newly added task and task priority information;
the dynamic allocation module 406 is configured to perform allocation processing based on a consistency policy on a current task according to the task priority information and a priority order from high to low; the allocation process based on the consistency policy includes: calculating bid and offer information of executing the current task by each unmanned platform, and achieving consensus by all unmanned platforms according to task type information of the current task and allocation of the bid and offer information of the current task to the current task by all unmanned platforms;
and the output module 408 is configured to complete dynamic allocation of all the newly added tasks when the total number of the reallocated tasks reaches the number of the newly added tasks, so as to obtain task reallocation scheme information.
The dynamic allocation module 406 is further configured to obtain a task execution path of each unmanned platform according to task pre-allocation scheme information; inserting the current task into the middle of two adjacent tasks in the task execution path of the current unmanned platform in sequence, and calculating the income generated by executing the current task when the current task is inserted into each position; and comparing the benefits at all possible insertion positions, setting the maximum of the benefits as a bidding price, and obtaining bidding price information of the current unmanned platform for executing the current task.
The dynamic allocation module 406 is further configured to insert the current task into the current unmanned platform task execution path between two adjacent tasks in order to calculate the benefit generated by the earliest execution time when the current task is executed at the current location.
The dynamic allocation module 406 is further configured to determine whether the capabilities of the current unmanned platform match; judging whether a road between the current task and the task before and after the insertion point is communicated or not; judging whether the current task is inserted into the current position to meet the time window requirement or not; if any of the above is not satisfied, the benefit of inserting the current task at the current location is set to 0.
The dynamic allocation module 406 is further configured to determine an earliest time at which the execution of the inserted task can begin when the previous task is executed by the current unmanned platform at the current insertion location; determining a latest time to execute the insertion task at the current insertion location without affecting a next task; if the earliest time is earlier than the latest time, the current task is judged to be inserted into the current position, so that the time window requirement can be met.
The dynamic allocation module 406 is further configured to determine task requirement information according to task type information of a current task; the task demand information comprises needed heterogeneous unmanned platform type information and corresponding quantity information; bidding the current task by the unmanned platform in a bidding auction stage to obtain bidding information; in the consistency stage, each unmanned platform and other unmanned platforms with reachable communication mutually transmit bidding information which bidding tasks are considered by the unmanned platforms to be executed according to the bidding information, and the unmanned platform with the highest bidding corresponding to the number is selected according to the bidding information and task demand information; consensus is reached on the allocation of current tasks by alternating iterations of the bidding auction phase and the uniformity phase.
For specific limitations of the dynamic task allocation device, reference may be made to the above limitation of the dynamic task allocation method, and no further description is given here. The various modules in the dynamic task allocation device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a dynamic task allocation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A method of dynamic task allocation, the method comprising:
Acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform;
When a new task is detected, task information is acquired; the task information comprises the number of newly added tasks, task type information of each newly added task and task priority information;
According to the task priority information, carrying out consistency policy-based allocation processing on the current task according to the order of the priority from high to low; the allocation process based on the consistency policy comprises the following steps:
S1, inserting a current task into the middle of two adjacent tasks in a task execution path of a current unmanned platform in sequence, and calculating the income generated by executing the current task when the current task is inserted into each position; calculating the generated benefits by the earliest execution time when the current task is executed at the current position; the earliest execution time of the current task is as follows:
;
Wherein, Representing the current unmanned platform/>After the mth task is executed, the inserted task/>Earliest start execution time,/>Representation/>Time to begin executing task mth task,/>Representing the duration of the mth task,Representing the task/>, obtained by Dijkstra algorithmAnd unmanned platform/>The shortest path length between the mth task of (a);
s2, comparing benefits at all possible insertion positions, setting the maximum of the benefits as a bidding price, and obtaining bidding price information of executing a current task by the current unmanned platform;
S3, all unmanned platforms agree on the distribution of the current task according to the task type information of the current task and the bid and offer information of the current task by all unmanned platforms;
and when the total number of the reassigned tasks reaches the number of the newly-added tasks, completing the dynamic assignment of all the newly-added tasks to obtain task reassignment scheme information.
2. The method of claim 1, wherein prior to inserting a current task in sequence between two adjacent tasks in the current unmanned platform task execution path, calculating the revenue generated by executing the current task when inserted into each location, further comprising:
Judging whether the capabilities of the current unmanned platform are matched;
Judging whether a road between the current task and the task before and after the insertion point is communicated or not;
judging whether the current task is inserted into the current position to meet the time window requirement or not;
If any of the above is not satisfied, the benefit of inserting the current task at the current location is set to 0.
3. The method of claim 2, wherein the determining whether the insertion of the current task at the current location meets the time window requirement comprises:
Determining the earliest time when the current unmanned platform performs the previous task at the current insertion position and can start to perform the insertion task;
determining a latest time to execute the insertion task at the current insertion location without affecting a next task;
and if the earliest time is earlier than the latest time, judging that the current task is inserted into the current position to meet the time window requirement.
4. The method of claim 1, wherein the consensus is reached by all unmanned platforms for the assignment of the current task based on the task type information of the current task and the bid offer information of the current task, comprising:
Determining task demand information according to task type information of a current task; the task demand information comprises needed heterogeneous unmanned platform type information and corresponding quantity information;
Bidding the current task by the unmanned platform in a bidding auction stage to obtain bidding information;
In the consistency stage, each unmanned platform and other unmanned platforms which can be reached by communication of the unmanned platform mutually transmit bidding information which bidding tasks are considered by the unmanned platform to be executed according to the bidding information, and the unmanned platform with the highest bidding corresponding to the number is selected according to the bidding information and the task demand information;
consensus is reached on the allocation of current tasks by alternating iterations of the bidding auction phase and the uniformity phase.
5. The method of any one of claims 1 to 4, wherein the heterogeneous unmanned platform comprises an unmanned aerial vehicle and an unmanned aerial vehicle.
6. A dynamic task allocation device, the device comprising:
The task pre-allocation module is used for acquiring a task list to be executed and a heterogeneous unmanned platform list of executable tasks, generating task pre-allocation scheme information through a pre-allocation algorithm, and executing the tasks by the heterogeneous unmanned platform;
The task information acquisition module is used for acquiring task information when detecting a new task; the task information comprises the number of newly added tasks, task type information of each newly added task and task priority information;
the dynamic allocation module is used for carrying out allocation processing based on a consistency policy on the current task according to the task priority information and the order of the priorities from high to low; the allocation process based on the consistency policy comprises the following steps:
S1, inserting a current task into the middle of two adjacent tasks in a task execution path of a current unmanned platform in sequence, and calculating the income generated by executing the current task when the current task is inserted into each position; calculating the generated benefits by the earliest execution time when the current task is executed at the current position; the earliest execution time of the current task is as follows:
;
Wherein, Representing the current unmanned platform/>After the mth task is executed, the inserted task/>Earliest start execution time,/>Representation/>Time to begin executing task mth task,/>Representing the duration of the mth task,Representing the task/>, obtained by Dijkstra algorithmAnd unmanned platform/>The shortest path length between the mth task of (a);
s2, comparing benefits at all possible insertion positions, setting the maximum of the benefits as a bidding price, and obtaining bidding price information of executing a current task by the current unmanned platform;
S3, all unmanned platforms agree on the distribution of the current task according to the task type information of the current task and the bid and offer information of the current task by all unmanned platforms;
And the output module is used for completing the dynamic allocation of all the newly-added tasks when the total number of the reallocated tasks reaches the number of the newly-added tasks, and obtaining task reallocation scheme information.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
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