Method and device for mission planning of unmanned swarm

Abstract

The application discloses a task planning method and device for an unmanned cluster, and the method comprises the following steps: acquiring a task information set to be completed and a capability index information set of the unmanned cluster; the capability index information set comprises a capability index value set of each unmanned target of the unmanned cluster; performing classification processing on the capability index information set of the unmanned cluster to obtain a plurality of sub-cluster index sets; the sub-cluster index set comprises a capability index value set of all unmanned targets contained in a sub-cluster; performing multi-level task planning processing on the task information set to be completed and the sub-cluster index set to obtain a task allocation information set.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned swarm and the field of information processing, and in particular to a task planning method and device for unmanned swarm. BACKGROUND

[0002] In the field of unmanned swarm cooperative operation, the rationality of task planning directly affects the operation efficiency and task success rate. However, the existing methods have significant defects in unmanned swarm task allocation:

[0003] Coarse division of swarm capability: Traditional methods are mostly based on single indicators (such as endurance time, load capacity) to classify unmanned devices, which is difficult to reflect the comprehensive differences of devices in multi-dimensional capabilities (communication, perception, execution). For example, in the complex terrain mapping task, dividing the swarm according to endurance capacity may lead to devices with insufficient perception accuracy undertaking high-precision mapping tasks, resulting in task failure.

[0004] Low efficiency of task and capability matching: Existing task planning relies on artificial preset rules or simple weight allocation, which cannot dynamically adapt to changes in task demand. When there are multiple index constraints (such as timeliness, accuracy, energy consumption) in the task, traditional methods are prone to "overload-idle" polarization problem, i.e., part of the devices are overloaded, while other devices are idle.

[0005] High computational complexity and poor scalability: When using global search or heuristic algorithms (such as genetic algorithm, ant colony algorithm) for task allocation, the computational complexity increases exponentially with the size of the swarm, making it difficult to meet real-time requirements; at the same time, when facing dynamic joining / leaving of devices or changes in task priority, it is difficult to adjust the algorithm, and it is difficult to quickly reconstruct the task allocation strategy.

[0006] Lack of data-driven intelligent decision-making: The existing scheme does not fully utilize the capability data and task historical data of unmanned devices, making it difficult to tap the collaborative potential among devices. For example, in emergency rescue tasks, it is difficult to identify efficient collaboration combinations based on historical collaboration data of devices, resulting in low resource collaboration efficiency. SUMMARY

[0007] The present application mainly solves the problem of how to effectively plan tasks for unmanned swarm based on different task types and requirements. The present application discloses a task planning method and device for unmanned swarm.

[0008] In a first aspect, the present application discloses a task planning method for unmanned swarm, comprising:

[0009] S1, acquire a task information set to be completed and a capability index information set of the unmanned cluster; the capability index information set comprises a capability index value set of each unmanned target of the unmanned cluster; the task information set to be completed comprises an index demand urgency set, an index demand value set and an index loss value set of each task to be completed;

[0010] S2, perform classification processing on the capability index information set of the unmanned cluster to obtain a plurality of sub-cluster index sets; the sub-cluster index set comprises a capability index value set of all unmanned targets contained in the sub-cluster;

[0011] S3, perform multi-level task planning processing on the task information set to be completed and the sub-cluster index set to obtain a task allocation information set.

[0012] The classification processing on the capability index information set of the unmanned cluster to obtain a plurality of sub-cluster index sets comprises:

[0013] S21, perform sub-cluster number calculation on the capability index information set of the unmanned cluster to obtain a total number of sub-clusters;

[0014] S22, based on the total number of sub-clusters, perform classification calculation processing on the capability index information set of the unmanned cluster to obtain a sub-cluster index set.

[0015] The sub-cluster number calculation on the capability index information set of the unmanned cluster to obtain a total number of sub-clusters comprises:

[0016] S211, represent the capability index information set of the unmanned cluster as a cluster capability index matrix; the row vector of the cluster capability index matrix is a vector composed of a capability index value set of one unmanned target; the element of the vector is each capability index value in the capability index value set;

[0017] S212, calculate the rank value a1 of the cluster capability index matrix;

[0018] S213, multiply the cluster capability index matrix by its transpose to obtain a first matrix;

[0019] S214, calculate the eigenvalue set of the first matrix;

[0020] S215, perform value distribution evaluation on the eigenvalue set to obtain a distribution evaluation value;

[0021] S216, determine the greatest common divisor of the distribution evaluation value and the rank value a1 as the total number of sub-clusters M.

[0022] The calculation expression of the value distribution evaluation is:

[0023]

[0024] wherein a2 is a distribution evaluation value, N is the number of characteristic values in the characteristic value set, and a, β, and γ are the mean, variance, and median value of all characteristic values in the characteristic value set, respectively, represents rounding up.

[0025] The classification and calculation processing of the capability index information set of the unmanned swarm based on the total number of sub-swarm is performed to obtain a sub-swarm index set, including:

[0026] S221, the capability index value set of each unmanned target in the capability index information set of the unmanned swarm is expressed as a capability vector; the element of the capability vector is each capability index value in the capability index value set;

[0027] S222, clustering analysis is performed on all capability vectors to obtain category information and capability vectors contained in each category information;

[0028] S223, the category information whose contained capability vector total number value size ranking is the first M is determined as M sub-swarm information; the unmanned target corresponding to the capability vector contained in the M category information is determined as the unmanned target contained in the M sub-swarm information;

[0029] S224, other capability vectors except the capability vectors contained in the M category information in all capability vectors are respectively subjected to attribution discrimination to obtain the category information to which the capability vector belongs, and the sub-swarm information of the unmanned target corresponding to the capability vector is determined according to the category information to which the capability vector belongs;

[0030] S225, the capability index value set of the unmanned target contained in all sub-swarm information is used to construct a sub-swarm index set; one sub-swarm index set corresponds to one sub-swarm.

[0031] The multi-level task planning processing of the to-be-completed task information set and the sub-swarm index set is performed to obtain a task allocation information set, including:

[0032] S31, the to-be-completed task information set and the sub-swarm index set are subjected to matching processing to obtain a sub-swarm matched with each to-be-completed task;

[0033] S32, task allocation is performed on the sub-swarm matched with each to-be-completed task to obtain a task allocation matrix of the sub-swarm;

[0034] S33, the task allocation information set is constructed by using the task allocation matrix of all sub-swarms.

[0035] The matching processing of the to-be-completed task information set and the sub-cluster index set obtains a sub-cluster matched with each to-be-completed task, and the matching processing comprises the following steps:

[0036] In S311, for each sub-cluster index set, the capability index value set of each unmanned target included in the sub-cluster index set is expressed as an index vector; and an element of the index vector is a capability index value in the capability index value set.

[0037] In S312, geometric center vector calculation is performed on all index vectors of the sub-cluster index set to obtain a corresponding center vector.

[0038] In S313, the index requirement urgency set of each to-be-completed task in the to-be-completed task information set is expressed as a corresponding urgency vector; and an element of the urgency vector is an urgency value of each index in the index requirement urgency set.

[0039] In S314, for each to-be-completed task, a vector distance between the urgency vector and the center vectors of all sub-cluster index sets is calculated; and a sub-cluster corresponding to a sub-cluster index set with a minimum vector distance is determined as a sub-cluster matched with the to-be-completed task.

[0040] In a second aspect of the embodiment of the present application, a task planning device of an unmanned cluster is disclosed, and the device comprises:

[0041] A memory in which an executable program code is stored;

[0042] A processor coupled with the memory;

[0043] The processor invokes the executable program code stored in the memory to execute the task planning method of the unmanned cluster.

[0044] In a third aspect of the embodiment of the present application, a computer storage medium is disclosed, and the computer storage medium stores computer instructions; when the computer instructions are invoked by a computer, the computer instructions are used to execute the task planning method of the unmanned cluster.

[0045] In a fourth aspect of the embodiment of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the task planning method of the unmanned cluster.

[0046] The present application has the following advantages:

[0047] The present application significantly improves the rationality and execution efficiency of the task planning of the unmanned cluster by using the innovative sub-cluster dynamic division and multi-level task planning strategy, and has the following specific advantages:

[0048] 1. Multi-dimensional capability precision classification capability of unmanned cluster performance; by converting capability index information into matrix form (S211), combining matrix rank (a1) and eigenvalue distribution evaluation (a2) to determine the total number of sub-clusters (S212-S216), fully considering the coupling relationship of multi-dimensional capabilities of unmanned equipment in communication, perception, execution, etc. For example, the rank value a1 reflects the linear independence of the capability index, and the distribution evaluation value a2 combines the sine function with the statistics (mean α, variance β, median γ) to quantify the concentration trend and dispersion degree of the eigenvalue distribution, avoiding classification bias caused by artificial preset cluster number.

[0049] 2. Self-adaptive clustering optimization resource allocation capability; based on the total number of sub-clusters M, perform clustering analysis (S221-S225), preferentially select the first M classes of capability vector size as the core sub-cluster, and dynamically allocate the remaining devices to the optimal sub-cluster through attribution discrimination. This strategy can effectively balance the resource load of each sub-cluster, for example, in the logistics distribution task, long-endurance and high-load devices are aggregated into a "long-distance transportation sub-cluster", and short-distance flexible devices form an "end distribution sub-cluster", improving the overall distribution efficiency.

[0050] 3. Multi-index dynamic matching capability: combined with the urgency, demand value and loss value of task index demand (S1), and multi-level matching with sub-cluster capability index (S31), to realize precise docking of task and device capability. For example, for tasks with high time efficiency requirements, preferentially match sub-clusters with strong communication capabilities and fast response speeds; for high-precision tasks, allocate sub-clusters with high perception accuracy to execute, reducing the risk of task failure. By constructing a sub-cluster task allocation matrix (S32), the priority and resource consumption of each sub-cluster in undertaking tasks are quantified, avoiding resource waste caused by traditional "one-size-fits-all" allocation. For example, using the Hungarian algorithm or auction algorithm to solve the task allocation matrix can minimize the total cost of task execution (such as energy consumption, time), improving the overall energy efficiency of the cluster.

[0051] 4. Improve computational efficiency and scalability: the sub-cluster division method based on matrix operations and statistical characteristics reduces the computational complexity from exponential to polynomial compared to traditional global search algorithms, meeting the real-time planning needs of large-scale clusters. At the same time, modular design supports dynamic addition or removal of devices or tasks. When new devices join, only the capability index matrix needs to be updated for quick reclassification without the need to retrain the model; by matching task index loss value with sub-cluster capability, task execution risk can be predicted in advance. For example, when the estimated loss of a sub-cluster in undertaking a task exceeds its capability threshold, the system automatically triggers task redistribution to avoid device overload damage or task interruption. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The flowchart of the method of the present application. DETAILED DESCRIPTION

[0053] For a better understanding of the present application, an embodiment is given here.

[0054] Figure 1 For the implementation flow chart of the method of the present application.

[0055] In the first aspect of the embodiment of the present application, a task planning method for unmanned swarm is disclosed, comprising:

[0056] S1, obtaining a set of to-be-completed task information and a set of capability index information of the unmanned swarm; the set of capability index information comprises a set of capability index values of each unmanned target of the unmanned swarm; the set of to-be-completed task information comprises a set of index demand urgency, a set of index demand values and a set of index loss values of each to-be-completed task;

[0057] S2, performing classification processing on the set of capability index information of the unmanned swarm to obtain a plurality of sets of sub-swarm indexes; each set of sub-swarm indexes comprises a set of capability index values of all unmanned targets contained in a sub-swarm;

[0058] S3, performing multi-level task planning processing on the set of to-be-completed task information and the sets of sub-swarm indexes to obtain a set of task allocation information;

[0059] The classification processing on the set of capability index information of the unmanned swarm to obtain a plurality of sets of sub-swarm indexes comprises:

[0060] S21, performing sub-swarm number calculation on the set of capability index information of the unmanned swarm to obtain a total number of sub-swarms;

[0061] S22, based on the total number of sub-swarms, performing classification calculation processing on the set of capability index information of the unmanned swarm to obtain the sets of sub-swarm indexes.

[0062] The sub-swarm number calculation on the set of capability index information of the unmanned swarm to obtain a total number of sub-swarms comprises:

[0063] S211, representing the set of capability index information of the unmanned swarm as a swarm capability index matrix; a row vector of the swarm capability index matrix is a vector composed of a set of capability index values of one unmanned target; an element of the vector is each capability index value in the set of capability index values;

[0064] S212, calculating a rank value a1 of the swarm capability index matrix;

[0065] S213, multiplying the swarm capability index matrix by its transpose to obtain a first matrix;

[0066] S214, calculate a set of eigenvalues of the first matrix;

[0067] S215, perform value distribution evaluation on the set of eigenvalues to obtain a distribution evaluation value;

[0068] S216, determine the greatest common divisor of the distribution evaluation value and the rank value a1 as the total number of sub-clusters M.

[0069] The calculation expression of the value distribution evaluation is:

[0070]

[0071] wherein a2 is the distribution evaluation value, N is the number of eigenvalues in the set of eigenvalues, and a, β and γ are the mean, variance and median of all eigenvalues in the set of eigenvalues respectively, represents rounding up.

[0072] The calculation expression of the value distribution evaluation dynamically quantifies the eigenvalue distribution pattern by combining the statistical characteristics of eigenvalues (the mean reflects the trend in the data set, the variance measures the degree of dispersion, and the median enhances robustness), using trigonometric functions and ratio operations. For example, when the eigenvalue distribution is concentrated, the calculation result tends to produce fewer sub-clusters, avoiding excessive division; on the contrary, when the distribution is discrete, the number of sub-clusters is increased, achieving fine classification. The rounding up operation ensures that the number of sub-clusters is an integer, adapting to the actual task requirements.

[0073] By introducing the median as a calculation parameter, compared with relying only on the mean and variance, the influence of abnormal eigenvalues (such as outliers caused by sensor noise) on the result can be effectively suppressed. For example, in a complex electromagnetic environment, there may be outliers in the device capability index data, and the use of the median makes the calculation result of a2 closer to the true distribution, ensuring the accuracy of sub-cluster division. Balance classification granularity and calculation efficiency: the number of eigenvalues N directly participates in the calculation, so that the number of sub-clusters is directly related to the data size: the larger the data size (the larger N), the more the calculation result tends to increase the number of sub-clusters, improving the classification granularity; at the same time, it avoids the surge in computational complexity caused by excessive division, achieving a balance between classification accuracy and computational efficiency in large-scale cluster scenarios.

[0074] The classification calculation processing of the capability index information set of the unmanned cluster based on the total number of sub-clusters obtains a sub-cluster index set, including:

[0075] S221, the capability index value set of each unmanned target in the capability index information set of the unmanned cluster is represented as a capability vector; the elements of the capability vector are each capability index value in the capability index value set;

[0076] S222, performing cluster analysis on all the capability vectors to obtain category information and capability vectors contained in each category information;

[0077] S223, determining category information whose contained capability vectors are ranked as the first M, as M subset cluster information; determining unmanned targets corresponding to the capability vectors contained in the M category information, as unmanned targets contained in the M subset cluster information;

[0078] S224, performing attribution discrimination on other capability vectors except the capability vectors contained in the M category information in all the capability vectors, respectively, to obtain the category information to which the capability vectors belong, and determining the subset cluster information of the unmanned target corresponding to the capability vectors according to the category information to which the capability vectors belong;

[0079] S225, constructing a subset cluster index set by using the capability index value set of the unmanned targets contained in all the subset cluster information. One subset cluster index set corresponds to one subset cluster.

[0080] One subset cluster index set corresponds to the capability index value set of the unmanned targets contained in one subset cluster information.

[0081] One subset cluster information, namely a subset cluster, includes a plurality of unmanned targets.

[0082] The multi-level task planning processing of the to-be-completed task information set and the subset cluster index set to obtain the task allocation information set includes:

[0083] S31, performing matching processing on the to-be-completed task information set and the subset cluster index set to obtain a subset cluster matched with each to-be-completed task.

[0084] S32, performing task allocation on the subset cluster matched with each to-be-completed task to obtain a task allocation matrix of the subset cluster.

[0085] S33, constructing a task allocation information set by using the task allocation matrix of all the subset clusters.

[0086] The matching processing of the to-be-completed task information set and the subset cluster index set to obtain a subset cluster matched with each to-be-completed task includes:

[0087] S311, for each subset cluster index set, representing the capability index value set of each unmanned target included in the subset cluster index set as an index vector; the element of the index vector is a capability index value in the capability index value set.

[0088] S312, performing geometric center vector calculation on all the index vectors of the subset cluster index set to obtain a corresponding center vector.

[0089] S313, the indicator demand urgency set of each to-be-completed task in the to-be-completed task information set is represented as a corresponding urgency vector; an element of the urgency vector is an urgency value of each indicator in the indicator demand urgency set;

[0090] S314, for each to-be-completed task, a vector distance between the urgency vector thereof and a center vector of all subset cluster indicator sets is calculated; and a subset cluster of a subset cluster indicator set corresponding to a smallest vector distance is determined as a subset cluster matched with the to-be-completed task;

[0091] The expression of the geometric center vector calculation is as follows:

[0092]

[0093] wherein a1 j is the jth element of the center vector, a ij is the jth element of the ith indicator vector of a subset cluster indicator set, and n is the total number of indicator vectors included in the subset cluster indicator set.

[0094] The geometric mean (root of product) is used to calculate the center vector, which can reflect the synergistic relationship and comprehensive level of each device capability indicator in the subset cluster more than the arithmetic mean. For example, in a surveying and mapping task, if a subset cluster includes a high-precision camera device (high-resolution indicator) and a long-endurance unmanned aerial vehicle (high-endurance indicator), the geometric mean can avoid the short board of other indicators (such as resolution) being covered by a single strong indicator (such as endurance), and ensure that the center vector fully represents the capability characteristics of the subset cluster.

[0095] The vector distance can be obtained by calculating the Frobenius norm of the difference vector of two vectors, or by using the Hamming distance and Jaccard coefficient to calculate.

[0096] The task allocation of each subset cluster matched with the to-be-completed task is performed to obtain a task allocation matrix of the subset cluster, including:

[0097] S321, for each subset cluster matched with the to-be-completed task, a corresponding task allocation model is constructed;

[0098] The expression of the task allocation model is as follows:

[0099]

[0100] wherein f(C) is an objective function, C is a task allocation matrix, c ik is an element of the ith row and the kth column of the matrix C, which takes a value of 0 or 1, and c ikWhen the value is 0, it indicates that the kth unmanned target of the sub-cluster does not participate in the ith step of the task to be completed, c ik When the value is 1, it indicates that the kth unmanned target of the sub-cluster participates in the ith step of the task to be completed, d kj sj represents the jth capability index value of the kth unmanned target of the sub-cluster, s i (C) and g i (C) respectively represent the loss sub-function and the benefit sub-function of the ith step of the task to be completed, N1 is the total number of steps, K is the total number of unmanned targets of the sub-cluster, M1 is the total number of capability index values, x ij fij represents the jth index demand value of the ith step of the task to be completed, f ij gij represents the jth index loss value of the ith step of the task to be completed.

[0101] S322, the task allocation model corresponding to the sub-cluster matched for each task to be completed is solved, and a task allocation matrix of the sub-cluster is obtained.

[0102] The task allocation model corresponding to the sub-cluster matched for each task to be completed can be solved by using an ant colony algorithm, a particle filtering algorithm, a wolf swarm algorithm, etc.

[0103] The index loss value and the index demand value of each step can be respectively obtained from the index loss value set and the index demand value set of each task to be completed.

[0104] The unmanned target is an unmanned device, including an unmanned vehicle, a drone, etc.

[0105] In a second aspect of the embodiment of the present application, a task planning device of an unmanned cluster is disclosed, and the device comprises:

[0106] A memory storing executable program codes;

[0107] A processor coupled with the memory;

[0108] The processor invokes the executable program codes stored in the memory to execute the task planning method of the unmanned cluster.

[0109] In a third aspect of the embodiment of the present application, a computer storage medium is disclosed, and the computer storage medium stores computer instructions, which are invoked by a computer to execute the task planning method of the unmanned cluster.

[0110] In a fourth aspect of the embodiment of the present application, an information data processing terminal is disclosed, which is used to implement the task planning method of the unmanned cluster.

[0111] The above merely illustrates the embodiments of the present application but should not be taken as limitations. Various changes and modifications can be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. A task planning method for an unmanned swarm, characterized in that, include: S1, obtain the set of information on tasks to be completed and the set of capability indicator information of the unmanned cluster; the set of capability indicator information includes the set of capability indicator values ​​for each unmanned target in the unmanned cluster; the set of information on tasks to be completed includes the set of urgency of indicator requirements, the set of indicator requirement values, and the set of indicator loss values ​​for each task to be completed. S2, classify the capability indicator information set of the unmanned cluster to obtain several sub-cluster indicator sets; the sub-cluster indicator set includes the capability indicator value set of all unmanned targets contained in the sub-cluster, including: S21, calculate the number of sub-clusters from the set of capability index information of the unmanned cluster to obtain the total number of sub-clusters, including: S211, the set of capability index information of the unmanned swarm is represented as a swarm capability index matrix; the row vector of the swarm capability index matrix is ​​a vector composed of a set of capability index values ​​of an unmanned target; the element of the vector is each capability index value in the set of capability index values. S212, calculate the rank value a1 of the cluster capability index matrix; S213, Multiply the cluster capability index matrix by its transpose to obtain the first matrix; S214, calculate the set of eigenvalues ​​of the first matrix; S215, evaluate the value distribution of the feature value set to obtain the distribution evaluation value; S216, determine the greatest common divisor of the distribution evaluation value and the rank value a1, which is the total number of sub-clusters M; S22, Based on the total number of sub-clusters, the capability indicator information set of the unmanned cluster is classified and calculated to obtain the sub-cluster indicator set; S3, perform multi-level task planning processing on the set of task information to be completed and the set of sub-cluster indicators to obtain a set of task allocation information.

2. The task planning method for unmanned swarms as described in claim 1, characterized in that, The calculation expression for the value distribution evaluation is as follows: Where a2 is the distribution evaluation value, N is the number of eigenvalues ​​in the eigenvalue set, and α, β, and γ are the mean, variance, and median values ​​of all eigenvalues ​​in the eigenvalue set, respectively. This indicates rounding up to the nearest integer.

3. The task planning method for unmanned swarms as described in claim 1, characterized in that, The capability indicator information set of the unmanned cluster is classified and calculated based on the total number of sub-clusters to obtain a sub-cluster indicator set, including: S221, the set of capability index values ​​for each unmanned target in the set of capability index information of the unmanned cluster is represented as a capability vector; the elements of the capability vector are each capability index value in the set of capability index values. S222, perform cluster analysis on all capability vectors to obtain category information and the capability vector contained in each category; S223, determine the total number of capability vectors included, and sort the top M categories by their values ​​to form M sub-cluster information; determine the unmanned targets corresponding to the capability vectors included in the M categories to form the unmanned targets included in the M sub-cluster information. S224, For all capability vectors other than the capability vectors contained in the M category information in all capability vectors, perform classification judgment to obtain the category information to which the capability vector belongs, and determine the sub-cluster information of the unmanned target corresponding to the capability vector based on the category information to which the capability vector belongs; S225, using the set of capability index values ​​of unmanned targets contained in all sub-cluster information, a sub-cluster index set is constructed; one sub-cluster index set corresponds to one sub-cluster.

4. The task planning method for unmanned swarms as described in claim 1, characterized in that, The process of performing multi-level task planning on the set of tasks to be completed and the set of sub-cluster indicators to obtain a task allocation information set includes: S31, perform matching processing on the set of information about tasks to be completed and the set of sub-clusters indicators to obtain the sub-clusters matched for each task to be completed; S32, assign tasks to each sub-cluster that matches the task to be completed, and obtain the task assignment matrix of the sub-cluster; S33. Using the task allocation matrix of all sub-clusters, a set of task allocation information is constructed.

5. The task planning method for unmanned swarms as described in claim 4, characterized in that, The process of matching the set of tasks to be completed and the set of sub-clusters indicators to obtain the sub-clusters matched for each task to be completed includes: S311, For each sub-cluster index set, the set of capability index values ​​for each unmanned target included in the sub-cluster index set is represented as an index vector; the elements of the index vector are the capability index values ​​in the set of capability index values. S312, calculate the geometric center vector of all indicator vectors in the sub-cluster indicator set to obtain the corresponding center vector; S313, the set of urgency requirements of each task to be completed in the set of task information to be completed is represented as a corresponding urgency vector; the elements of the urgency vector are the urgency values ​​of each indicator in the set of urgency requirements. S314. For each task to be completed, calculate the vector distance between its urgency vector and the center vector of all sub-cluster indicator sets; determine the sub-cluster corresponding to the sub-cluster indicator set with the smallest vector distance, which is the sub-cluster that matches the task to be completed.

6. A task planning device for an unmanned swarm, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the unmanned swarm task planning method as described in any one of claims 1 to 5.

7. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the unmanned swarm task planning method as described in any one of claims 1 to 5.

8. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the task planning method for unmanned swarms as described in any one of claims 1 to 5.

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