CN115511268A - Inter-group task resource coordination distribution method for unmanned cluster command control - Google Patents
Inter-group task resource coordination distribution method for unmanned cluster command control Download PDFInfo
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Abstract
The invention discloses an inter-group task resource coordination distribution method for unmanned cluster command control, which belongs to the technical field of unmanned cluster command control and comprises the following steps: extracting all unmanned cluster tasks and corresponding requirements of all tasks, and sequentially extracting task activity sequences of the tasks and resource requirements of all task activities based on respective meta-task models of the tasks; calculating feasible unmanned clusters of each single task based on resource requirements and available task resource sets to obtain a multi-task candidate unmanned cluster list; based on the multitask candidate unmanned cluster list, when the contention resources exist, based on the maximization of the overall benefit of the formation task, the automatic coordination and distribution of the contention resources among the unmanned clusters are realized, and when the contention resources do not exist, the unmanned cluster with the highest comprehensive evaluation value is selected as a task resource distribution result. The method for distributing the task resources among the unmanned aerial vehicle cluster, the unmanned ship cluster and the unmanned underwater vehicle cluster under the multi-domain and multi-task conditions can be realized.
Description
Technical Field
The invention belongs to the technical field of unmanned cluster command control, and particularly relates to a task resource allocation method among unmanned clusters.
Background
The generalization requires that the unmanned combat system can be matched with enough mission loads and meet the requirements of various tactics. The integration means that the unmanned combat system develops towards multi-platform, clustered cooperation and multi-system cooperation, and not only can independently execute tasks but also can fight in a coordinated manner.
Under the development trend of intellectualization, universalization and integration of unmanned combat systems, command control capability matched with the unmanned combat systems is required, wherein task resource allocation is one of key problems to be solved by unmanned cluster command control. In the existing research scheme, from the content, task resource allocation also focuses on task resource coordination allocation in an unmanned cluster; the research results of the inter-group task resource coordination distribution are less; from the technical approach, optimization scheme solution is mostly performed through a genetic algorithm, a particle swarm algorithm, a tabu search algorithm, a simulated annealing algorithm, a market auction mechanism and the like, and when tasks are multiple, task activity sequences are complex, task constraints are multiple, and task resources are combined, problems that optimization solution cannot be achieved within effective time, various task scenes cannot be met, resource combination errors or omissions are caused due to insufficient consideration of task constraints exist, and the like. Therefore, an efficient and general solution is lacking for the inter-group task resource coordination and allocation under the multi-domain and multi-task clustered cooperation condition.
Disclosure of Invention
Aiming at the task conflict problem possibly generated when the unmanned cluster resources are contended and robbed in multi-domain operation and the conflict problem in task allocation, the invention provides an unmanned cluster command control-oriented inter-group task resource coordination allocation method, which is suitable for the inter-group task resource allocation method of unmanned clusters, unmanned ship groups and unmanned underwater vehicles under the multi-domain and multi-task conditions, can realize optimized solution within effective time and has applicability under various task scenes, thereby improving the cooperative task decision-making capability and decision-making efficiency.
In order to achieve the above object, the present invention provides an inter-group task resource coordination distribution method facing unmanned cluster command control, which comprises:
(1) Accessing and analyzing a formation level task plan, automatically extracting all unmanned cluster tasks and corresponding requirements of each task, and sequentially extracting task activity sequences of the tasks and resource requirements of each task activity for each task based on respective meta-task models of the tasks;
(2) Based on the resource requirements of the extracted task activities and available task resource sets, automatically calculating feasible unmanned clusters of each single task in sequence, traversing all tasks, and summarizing to obtain a multi-task candidate unmanned cluster list;
(3) The method comprises the steps of automatically detecting multitask contention and robbery resources based on a multitask candidate unmanned cluster list, realizing automatic coordinated distribution of the contention and robbery resources among unmanned clusters based on the maximization of the overall benefit of a formation task when the contention and robbery resources exist, and directly selecting the unmanned cluster with the highest comprehensive evaluation value as a task resource distribution result when the contention and robbery resources do not exist;
(4) And displaying the task resource allocation result in a form of a graph and a table based on the automatically generated task resource coordination allocation among the groups, supporting the manual adjustment of the resource allocation, and finally forming a queue-level task resource allocation scheme.
In some alternative embodiments, step (1) comprises:
(1.1) analyzing multi-domain, grouping-level task information in the grouping-level task plan, wherein each task analysis extraction content comprises: the method comprises the following steps of task numbering, task names, task domains, task types, task time, task areas and task targets;
(1.2) extracting the task activity sequence of each task based on the task activity sequence description in the corresponding meta-task model of each task to obtain the task activity name and the time sequence relation of each task, wherein the time sequence relation can be parallel or serial;
and (1.3) extracting task resource requirements based on task activity resource requirements in the corresponding meta-task model of each task.
In some alternative embodiments, step (2) comprises:
(2.1) based on task information, task activity resource requirements and available task resource sets, according to the granularity of task activities, integrating task capacity and integrated task capacity index requirements, task platform types or models and quantity requirements of the task platform types or models, task load types or models and quantity requirements of the task load types or models, task time constraints, task space constraints and task activity sequence timing constraints, automatically calculating all candidate unmanned clusters meeting task requirements, and calculating single evaluation and integrated evaluation values of task satisfaction and task resource costs of the candidate unmanned clusters;
and (2.2) traversing all the tasks, and summarizing the candidate unmanned clusters corresponding to the single tasks to obtain a multitask candidate unmanned cluster list.
In some alternative embodiments, step (2.1) comprises:
(2.1.1) acquiring task activity resource requirements;
(2.1.2) according to the task activity resource requirements, screening resources meeting task capacity requirements, task platform requirements and task load requirements from the formation available task resources in a centralized manner;
(2.1.3) traversing all the activities in the task to obtain an available resource set of all the activities of the task;
(2.1.4) establishing a relation matrix between task activities and available task resources according to a task activity sequence time sequence;
(2.1.5) searching available unmanned clusters in the relation matrix based on the task activity sequence time sequence relation, the task time constraint and the task space constraint to form candidate unmanned clusters;
and (2.1.6) respectively calculating the task satisfaction degree of each candidate unmanned cluster, the single evaluation of the task resource cost and the comprehensive evaluation value of each candidate unmanned cluster.
In some alternative embodiments, step (3) comprises:
(3.1) automatically detecting and marking the contended resources in the multitask candidate unmanned cluster list, wherein the judgment basis is as follows: whether the task platforms with the same model are selected as candidate resources by a plurality of tasks or not, if not, the resources for contention and robbery do not exist; otherwise, further judging whether the quantity of the resources selected as candidates by the multiple tasks can simultaneously execute the multiple tasks without the problems of quantity support, time conflict, space conflict and capacity unsupported, if so, not contending for the resources; otherwise, prompting that the resource is contended;
(3.2) establishing an optimal distribution objective function based on the overall benefit maximization of the formation tasks, realizing the automatic coordination distribution of the contended resources among the unmanned clusters only in the task set with the contended resources, and directly adopting a traversal combination mode to find a coordination distribution scheme for maximizing the objective function when the number of the candidate unmanned clusters to be subjected to coordination distribution is less than the set number; otherwise, a heuristic algorithm is adopted to search a coordination distribution scheme which maximizes the objective function.
In some alternative embodiments, step (3.1) comprises:
(3.1.1) judging whether the task platforms with the same model are selected as candidate resources by a plurality of tasks, if not, the resources are not contended for; otherwise, marking as possible to contend for resources;
(3.1.2) judging whether the available quantity of the possibly contended resources is larger than the sum of the required quantity of the multi-task resources, if so, not competing for the resources; otherwise, marking as possible to contend for resources;
(3.1.3) judging whether time of executing a plurality of tasks possibly competing for resources is overlapped, if the time is not overlapped, further judging whether the space position of the task area is accessible, and if the space position is accessible, not competing for the resources; otherwise, marking as possible to contend for resources; if the multiple tasks overlap in time, further judging whether the task area spaces overlap and the resource task capability supports simultaneous support of the multiple tasks, and if the task areas overlap and the resource task capability supports simultaneous multitask, contention for resources does not exist; otherwise, the label is a potential contention for the resource.
In some alternative embodiments, the meta-tasks of the meta-task model represent tasks that can be independently performed by an unmanned grouping, the meta-tasks comprising one or more task activities that can be independently performed by a single unmanned platform; the meta-task model adopts a generalized task description mechanism, and one type of task defines a meta-task model which comprises task basic information, task execution parameters, task execution conditions, task resource requirements and task planning model information.
In some alternative embodiments, the task resource requirements in the meta-task model support three modes based on task capabilities and indices, based on task platform type or model and number of task platform types or models, based on task load type or model and number of task load types or models, and can describe resource requirements in one or more modes.
In some optional embodiments, the available task resource set in step (2) is constructed according to a task platform, and the task resource of each model in the task resource set includes a name, a platform model, a platform type, a platform task capability, a platform task load type, a model of the platform task load type, and available quantity information.
In some optional embodiments, there are one or more candidate unmanned clusters in step (2) that support three cluster types, unmanned fleet, unmanned underwater vehicle fleet; supporting an isomorphic and heterogeneous unmanned platform in a group; each candidate unmanned cluster contains information on the type of resource and the amount of the resource.
In some optional embodiments, the task satisfaction degree in the step (2) can be based on measures such as resource capacity, task expected benefit and the like; the task resource cost can be based on measurement modes such as time consumption, resource usage amount and total range number; the integrated evaluation value can be based on a weighted integrated value of the individual evaluation results.
In some alternative embodiments, the heuristic algorithm in step (3) may be based on simulated annealing algorithms, genetic algorithms, list search algorithms, evolutionary programming, ant colony algorithms, and the like.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the invention provides a task resource coordination distribution decision method among groups based on task overall benefit maximization, aiming at the task conflict problem possibly generated when the unmanned cluster resources are contended and robbed in multi-domain operation and the conflict problem in task distribution, comprehensively considering the factors of task activity resource demand, task time constraint, task space constraint, resource quantity and task capacity constraint, task benefit and the like, and can reduce the dimension of the task resource distribution problem into the resource allocation problem for contending and robbing so as to realize the high-efficiency and universal task resource coordination distribution among the unmanned cluster, unmanned ship group and unmanned underwater vehicle group under the multi-domain and multi-task condition and improve the cooperative task decision capability and decision efficiency.
Drawings
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a task candidate unmanned cluster computing step according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an exemplary relationship matrix in a task candidate unmanned cluster computing step according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a step of coordinating and allocating task resources among groups according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the respective embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides an inter-group task resource coordination distribution method oriented to unmanned cluster command control, which realizes the coordination distribution of task resources among unmanned cluster, unmanned ship group and unmanned underwater vehicle group under the condition of multiple domains and multiple tasks. The method specifically comprises the following steps: the method comprises four steps of task resource demand extraction, task candidate unmanned cluster calculation, task resource coordination distribution among clusters and task resource distribution scheme generation, and the overall flow is shown in figure 1.
Take the coordinated allocation of task resources among unmanned cluster groups of a formation-level task plan P as an example (the formation-level task plan comprises two domain tasks, respectively denoted as D) 1 、D 2 (ii) a Wherein D is 1 Comprising a marshalling task
D 2 Comprising three marshalling tasks
The formation may use 7 types of task resources, which are respectively recorded as: r is 1 、R 2 、R 3 、R 4 、R 5 、R 6 、R 7 ) The technical scheme of the invention is elaborated as follows:
1) Task resource demand extraction
Accessing and analyzing the formation level task plan P, and automatically extracting all the unmanned cluster tasks
And the requirements thereof; and aiming at each task, sequentially extracting the task activity sequence of each task and the resource requirement of each task activity based on the respective meta-task model. The method specifically comprises three sub-steps of task extraction, task activity sequence extraction and task activity resource requirement extraction:
A. task extraction: resolving multi-domain, group-level tasks in a group-level task plan
Task information of (1). Each task parsing and extracting content comprises the following steps: task number T _ num, task name T _ name, task Domain T _ Domain, task Type T _ Type, and task time< T _ ts | T _ preT, T _ te >, task area < T _ r 1 ,T_r 2 The math, task object < T _ T 1 ,T_t 2 A. Wherein T _ ts represents a start time; t _ preT represents a pre-task; t _ te represents the end time; t _ r represents a single region, one task supporting one or more regions; t _ T denotes a single target, with one task supporting one or more targets.
B. Task activity sequence extraction: extracting task activity sequences of the tasks based on task activity sequence description in the corresponding meta-task model of each task to obtain task activity names and time sequence relations of the task activity names; the timing relationship may be parallel or serial.
In the present embodiment of the present invention,
the task includes, for example, 3 serial task activities, which are respectively denoted as:
the task includes, for example, 3 parallel task activities, which are respectively denoted as:
the task example includes 1 task activity, which is recorded as:
the task example includes 1 task activity, which is recorded as:
C. task activity resource requirement extraction: and extracting task resource requirements based on task activity resource requirements in a meta-task model corresponding to each task (the meta-task model adopts a generalized task description mechanism, one type of task defines one meta-task model and comprises task basic information, task execution parameters, task execution conditions, task resource requirements and task planning model information).
The task resource demand in the meta-task model supports three modes of task capacity and index, task platform type or model and number thereof and task load type or model and number thereof, and the resource demand can be described according to one or more modes. Wherein the capacity index ranges from 0 to 1; the number is a number greater than 1. In the embodiment, tasks are used
For example, the task activity resource requirements are as follows:
C A1 、C A2 、C A3 indicating a resource requirement based on task capability, (-) indicating no requirement for a capability index, (0.9) indicating that the capability index should be greater than 0.9; p A1 Representing resource requirements based on task platform type, (2) representing quantity requirements of 2; l is A3 Indicating resource requirements based on task load type, (-) indicating no quantity requirement; nuLL indicates no requirement, i.e., no restriction, in this mode.
2) Task candidate unmanned cluster computing
Based on the extracted task activity resource requirements and available task resource sets, automatically calculating feasible unmanned clusters of each single task in sequence; traversing all tasks, summarizing to obtain a multitask candidate unmanned cluster list, specifically comprising two substeps of calculating a single task candidate unmanned cluster and generating the multitask candidate unmanned cluster list, wherein the process is shown in fig. 2.
A. Calculating a single task candidate unmanned cluster: based on task information, task activity resource requirements and available task resource sets, according to the granularity of task activities, the comprehensive task capacity and index requirements, the type or model of a task platform and quantity requirements thereof, the type or model and quantity requirements thereof of task loads, task time constraints, task space constraints and task activity sequence time sequence constraints, automatically calculating all candidate unmanned clusters meeting the task requirements, and calculating single evaluation and comprehensive evaluation values of the task satisfaction, task resource costs and the like, the method specifically comprises the following steps:
a) Acquiring the task activity resource requirements extracted in the step 1) C;
b) The set of task resources available from the formation (in this example, including type 7 available resources, R) is based on the task activity resource requirements 1 、R 2 、R 3 、R 4 、R 5 、R 6 、R 7 ) Screening resources meeting task capacity requirements, task platform requirements and task load requirements; by task
Movement of a movable part
By way of example, according to their resource requirements
Screening out resource R 1 、R 2 、R 7 The requirements are met;
c) Repeating A.a) -2). A.b) of step 2) until all active traversals of the task are completed. In this embodiment, a task is obtained
Movement of
The available resource of is R 1 、R 2 、R 7 (ii) a Movement of a movable part
Is R 1 、R 4 (ii) a Movement of
Is resource R 1 、R 2 、R 3 。
d) And establishing a relation matrix between the task activities and the available task resources according to the task activity sequence time sequence. By a task
For example, the relationship matrix is shown in FIG. 3, where "0" indicates that the resource is unavailable; "1" indicates that the resource is available.
e) Based on the time sequence relation of task activity sequence, the constraint of task time < T _ ts | T _ preT, T _ te >, and the constraint of task space < T _ r 1 ,T_r 2 A. >, in 2) a.d) to search for available unmanned clusters in the obtained relationship matrix, and form candidate unmanned clusters. In the embodiment, tasks are used
Get tasks for example
The candidate unmanned cluster of (a) is:
first candidate unmanned cluster
Second candidate unmanned cluster
Third candidate unmanned Cluster
Fourth candidate unmanned cluster
f) And respectively calculating single evaluation such as task satisfaction, task resource cost and the like of each candidate unmanned cluster and a comprehensive evaluation value E thereof. In the embodiment, the task satisfaction degree is calculated based on the resource capacity index; calculating the task resource cost based on the resource value multiplied by the resource quantity; and the comprehensive evaluation value is obtained by weighted fusion calculation of the task satisfaction degree and the task resource cost value, wherein the fusion weight values of the task satisfaction degree and the task resource cost are 0.7 and 0.3 respectively.
B. Generating a multitask candidate unmanned cluster list: and C, repeating the step A to traverse all tasks, summarizing the calculation of the single task candidate unmanned cluster, and obtaining a multi-task candidate unmanned cluster and a comprehensive evaluation value list thereof as follows:
3) Inter-group task resource coordination allocation
And (3) coordinating and distributing task resources among groups: based on the multitask candidate unmanned cluster list, automatic multitask contention and resource detection is realized; when the contended resources exist, the overall benefit is maximized based on the formation task, and the automatic coordination and distribution of the contended resources among the unmanned clusters are realized; and when the contention resources do not exist, directly selecting the unmanned cluster with the highest comprehensive evaluation value as a task resource allocation result. Specifically, the method includes two sub-steps of detecting the contended resources and coordinating and allocating the contended resources, and the flow is shown in fig. 4.
A. Detecting the contended resources: and automatically detecting and marking the contended resources in the multitask candidate unmanned cluster list. The judgment basis is as follows: whether the task platforms with the same model are selected as candidate resources by a plurality of tasks or not, if not, the resources for contention and robbery do not exist; otherwise, further judging whether the quantity of the resources selected as candidates by the multiple tasks can simultaneously execute the multiple tasks without the problems of quantity support, time conflict, space conflict and capacity unsupported, if so, not contending for the resources; otherwise, the existence of the resource contention is prompted.
a) Judging whether the task platforms with the same model are selected as candidate resources by a plurality of tasks, if not, the resources for contention and robbery do not exist; otherwise, the label is a potential contention for the resource. In this embodiment, resource R 1 、R 4 、R 7 Whether a task platform of the same model is selected as a candidate resource by a plurality of tasks, namely R 1 、R 4 、R 7 May be a contention for resources.
b) Judging whether the available quantity of the possibly contended resources in the step 3) and A.a) is larger than the sum of the required quantity of the multi-task resources, if so, not contending for the resources; otherwise, the resource is marked as possible to be contended. In this embodiment, resource R 1 、R 4 Is less than the total number of the demands of the plurality of task resources; resource R 7 The available number is greater than the total number of the plurality of task resource demands. Whereby R 1 、R 4 It is still a possible contention for resources.
c) Judging whether the time for executing a plurality of tasks possibly contending for the resources in the step 3) or A.b) is overlapped, if not, further judging whether the spatial position of the task area is accessible, and if so, not, judging that the resources are not contended for; otherwise, the label is a potential contention for the resource. If a plurality of tasks overlap in time, further determining taskWhether the service area spaces are overlapped and the resource task capability supports the multiple tasks at the same time, if the service area spaces are overlapped and the resource task capability supports the multiple tasks at the same time, the resource contention does not exist; otherwise, the resource is marked as possible to be contended. In this embodiment, the resource R may be contended for 4 Corresponding task
And
time is not overlapped, and the space position of the task area can be reached; but may contend for resource R 1 Task of (2)
And
time non-overlapping but resource R 1 The maneuvering speed is slow, and the spatial position of the task area is not accessible. Whereby R 1 In order to compete for resources.
B. And (3) allocating the contention resources in a coordinated manner: and (3) establishing an objective function of optimized allocation based on the overall benefit maximization of the formation tasks, and realizing the automatic coordination allocation of the contention resources among the unmanned clusters only in the task set with the contention resources. When the number of candidate unmanned clusters to be coordinated and distributed is smaller than the set number, directly adopting a traversal combination mode to search a coordination and distribution scheme for maximizing a target function; otherwise, a heuristic algorithm is adopted to find a coordination distribution scheme which maximizes the objective function.
a) And extracting a task set corresponding to the contended resources and a candidate unmanned cluster thereof. In this embodiment, the contending resource is R 1 Extracting a task set to be coordinated and distributed and a candidate unmanned cluster thereof as follows:
b) And establishing an objective function of resource optimization allocation. In the present embodiment, the comprehensive evaluation value in 2) a.f) is used as the objective function.
c) In this embodiment, the number of candidate unmanned clusters is less than the set number 20, and a traversal combination manner is directly adopted to find a coordination allocation scheme that maximizes an objective function, so that:
d) In other tasks without competing for resources, the unmanned cluster with the highest comprehensive evaluation value is directly selected as a task resource allocation result, and the inter-cluster task resource coordination allocation scheme based on the task overall benefit maximization in the embodiment is obtained as follows:
4) Task resource allocation scheme generation
Displaying the task resource allocation result in a form of a graph or a table based on the inter-group task resource coordination allocation result automatically generated in the step 3), supporting manual adjustment of resource allocation, and finally forming a formation-level task resource allocation scheme.
The invention provides a task resource coordination distribution decision method among groups based on task overall benefit maximization, aiming at the conflict problem of competition and task distribution of unmanned cluster resources in multi-domain operation, comprehensively considering factors such as task activity resource demand, task time constraint, task space constraint, resource quantity and task capacity constraint, and task benefits, reducing the dimension of the task resource distribution problem into the competition resource distribution problem, realizing efficient and universal coordination distribution of task resources among unmanned cluster, unmanned boat group and unmanned underwater vehicle group under the multi-domain and multi-task conditions, and improving the cooperative task decision capability and decision efficiency.
It should be noted that, according to implementation requirements, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can also be combined into a new step/component to achieve the purpose of the present invention.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An inter-group task resource coordination distribution method for unmanned cluster command control is characterized by comprising the following steps:
(1) Accessing and analyzing a formation-level task plan, automatically extracting all unmanned cluster tasks and corresponding requirements of all tasks, and sequentially extracting task activity sequences of the tasks and resource requirements of all task activities based on respective meta-task models of the tasks aiming at each task;
(2) Based on the resource requirements of the extracted task activities and available task resource sets, automatically calculating feasible unmanned clusters of each single task in sequence, traversing all tasks, and summarizing to obtain a multi-task candidate unmanned cluster list;
(3) The method comprises the steps of automatically detecting multitask contention and robbery resources based on a multitask candidate unmanned cluster list, realizing automatic coordinated distribution of the contention and robbery resources among unmanned clusters based on the maximization of the overall benefit of a formation task when the contention and robbery resources exist, and directly selecting the unmanned cluster with the highest comprehensive evaluation value as a task resource distribution result when the contention and robbery resources do not exist;
(4) And displaying a task resource allocation result based on the automatically generated task resource coordination allocation among the groups, supporting the manual adjustment of the resource allocation, and finally forming a formation level task resource allocation scheme.
2. The method of claim 1, wherein step (1) comprises:
(1.1) analyzing multi-domain, grouping-level task information in the grouping-level task plan, wherein each task analysis extraction content comprises: the method comprises the following steps of task numbering, task names, task domains, task types, task time, task areas and task targets;
(1.2) extracting the task activity sequence of each task based on the task activity sequence description in the corresponding meta-task model of each task to obtain the task activity name and the time sequence relation of each task, wherein the time sequence relation can be parallel or serial;
and (1.3) extracting task resource requirements based on the task activity resource requirements in the corresponding meta-task model of each task.
3. The method of claim 1 or 2, wherein step (2) comprises:
(2.1) based on the task information, the task activity resource requirements and available task resource sets, automatically calculating all candidate unmanned clusters meeting the task requirements according to the granularity of task activity, comprehensive task capacity and comprehensive task capacity index requirements, the number requirements of the types or models of task platforms, the number requirements of the types or models of task loads, task time constraints, task space constraints and task activity sequence time sequence constraints, and calculating the task satisfaction degree of the candidate unmanned clusters and the single evaluation and comprehensive evaluation value of the task resource cost;
and (2.2) traversing all the tasks, and summarizing the candidate unmanned clusters corresponding to the single tasks to obtain a multitask candidate unmanned cluster list.
4. The method of claim 3, wherein step (2.1) comprises:
(2.1.1) acquiring task activity resource requirements;
(2.1.2) according to the task activity resource requirements, screening resources meeting task capacity requirements, task platform requirements and task load requirements from the formation available task resources in a centralized manner;
(2.1.3) traversing all the activities in the task to obtain an available resource set of all the activities of the task;
(2.1.4) establishing a relation matrix between task activities and available task resources according to a task activity sequence time sequence;
(2.1.5) searching available unmanned clusters in the relation matrix based on the task activity sequence time sequence relation, the task time constraint and the task space constraint to form candidate unmanned clusters;
and (2.1.6) respectively calculating the task satisfaction degree of each candidate unmanned cluster, the single evaluation of the task resource cost and the comprehensive evaluation value of each candidate unmanned cluster.
5. The method of claim 4, wherein step (3) comprises:
(3.1) automatically detecting and labeling the contended resources in the multitask candidate unmanned cluster list, wherein the judgment basis is as follows: whether the task platforms with the same model are selected as candidate resources by a plurality of tasks or not, if not, the resources for contention and robbery do not exist; otherwise, further judging whether the quantity of the resources selected as candidates by the multiple tasks can simultaneously execute the multiple tasks without the problems of quantity support, time conflict, space conflict and capacity unsupported, if so, not contending for the resources; otherwise, prompting that the resource is contended;
(3.2) establishing an optimal distribution objective function based on the overall benefit maximization of the formation tasks, realizing the automatic coordination distribution of the contended resources among the unmanned clusters only in the task set with the contended resources, and directly adopting a traversal combination mode to find a coordination distribution scheme for maximizing the objective function when the number of the candidate unmanned clusters to be subjected to coordination distribution is less than the set number; otherwise, a heuristic algorithm is adopted to find a coordination distribution scheme which maximizes the objective function.
6. The method of claim 5, wherein step (3.1) comprises:
(3.1.1) judging whether the task platforms in the same model are selected as candidate resources by a plurality of tasks, if not, the resources for contention and robbery do not exist; otherwise, marking as possible to contend for resources;
(3.1.2) judging whether the available quantity of the possibly contended resources is larger than the sum of the required quantity of the multi-task resources, if so, not competing for the resources; otherwise, marking as possible to contend for resources;
(3.1.3) judging whether time of executing a plurality of tasks possibly competing for resources is overlapped, if the time is not overlapped, further judging whether the space position of the task area is accessible, and if the space position is accessible, not competing for the resources; otherwise, marking as possible to contend for resources; if the multiple tasks are overlapped in time, further judging whether the task area spaces are overlapped and the resource task capability supports simultaneous support of the multiple tasks, and if the task areas are overlapped and the resource task capability supports simultaneous multitask, not contending for resources; otherwise, the label is a potential contention for the resource.
7. The method of claim 1, wherein the meta-tasks of the meta-task model represent tasks that can be independently executed without human grouping, and the meta-tasks comprise one or more task activities that can be independently executed by a single unmanned platform; the meta-task model adopts a generalized task description mechanism, one type of task defines a meta-task model, and the meta-task model comprises task basic information, task execution parameters, task execution conditions, task resource requirements and task planning model information;
the task resource demand in the meta-task model supports three modes of task capacity and index, task platform type or model and number of task platform types or models, and task load type or model and number of task load types or models, and can describe the resource demand according to one or more modes.
8. The method according to claim 4, wherein the available task resource set in step (2) is constructed according to a task platform, and the task resource of each model in the task resource set comprises information of a name, a platform model, a platform type, platform task capability, a platform task load type, a model of the platform task load type, and available quantity; one or more candidate unmanned clusters exist, and three cluster types of an unmanned aerial vehicle cluster, an unmanned ship cluster and an unmanned underwater vehicle cluster are supported; supporting an isomorphic and heterogeneous unmanned platform in a group; each candidate unmanned cluster contains information on the type of resource and the amount of the resource.
9. The method of claim 8, wherein the task satisfaction in step (2) can be based on a measure of resource capacity and task expected benefit; the task resource cost can be based on a measurement mode of time consumption, resource usage and total range number; the integrated evaluation value can be based on a weighted integrated value of the individual evaluation results.
10. The method of claim 5, wherein the heuristic algorithm of step (3) can be based on simulated annealing, genetic algorithms, list search algorithms, evolutionary programming, and ant colony algorithms.
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