CN114529205B

CN114529205B - Mosaic warfare system capacity demand satisfaction evaluation method and related equipment

Info

Publication number: CN114529205B
Application number: CN202210162502.9A
Authority: CN
Inventors: 杨志伟; 刘麦笛; 向竹; 杨克巍; 陈刚; 杨第聪; 张昌哲; 龚常; 欧萌歆
Original assignee: National University of Defense Technology
Current assignee: National University of Defense Technology
Priority date: 2022-02-22
Filing date: 2022-02-22
Publication date: 2023-05-12
Anticipated expiration: 2042-02-22
Also published as: CN114529205A

Abstract

The application provides a mosaic warfare system capacity demand satisfaction evaluation method and related equipment. The method comprises the following steps: building a mosaic system super-network model according to the fight equipment of the mosaic system; receiving the issued combat resolution, determining a mission task according to the combat resolution, decomposing the mission task, logically combing the decomposition result to obtain a task hierarchical decomposition structure, and deploying the task hierarchical decomposition structure in a task layer in a task activity network mode; determining system combat capability meeting mission tasks, and constructing a capability requirement tree network based on a task hierarchical decomposition structure to be deployed in a capability layer; according to the task activity network deployed in the task layer and the capability requirement tree network deployed in the capability layer, determining an optimal cluster grouping strategy corresponding to the mosaic war system and deploying the optimal cluster grouping strategy in the cluster layer; and analyzing the demand satisfaction degree of the cluster performance indexes in the cluster layer on the capability layer, and carrying out upward aggregation on the demand satisfaction degree based on the capability demand tree network to obtain a capability demand satisfaction degree evaluation result.

Description

Mosaic warfare system capacity demand satisfaction evaluation method and related equipment

Technical Field

The application relates to the technical field of combat data analysis, in particular to a mosaic combat system capacity demand satisfaction evaluation method and related equipment.

Background

The large-sized high-end weapon equipment has the inherent characteristics of long research and development period, high research and development cost, weak environmental adaptability, strong system dependence and the like, and has limited strategic value and deterrent effect in future battlefields. The rapid development of emerging military technologies with artificial intelligence as a core promotes the progress of new rounds of military transformation, and small intelligent unmanned equipment with low cost, rapid development and flexible combination is gradually replacing the dominant position of large high-end weapon equipment in future battlefields. The key idea of mosaic battle is to take actual battle task as guide, realize autonomous grouping cooperation of a large number of small intelligent weaponry with low cost and single function, construct a combined battle system with on-demand integration, great elasticity and adaptability, and form a highly dispersed, flexible and tough battle 'killing net' with dynamic combination.

The existing capability demand satisfaction evaluation method facing the traditional combat system mainly aims at evaluating the satisfaction degree of equipment performance indexes on task capability demands, and lacks consideration of cluster cooperative strategy factors. The method is directly applied to capability demand satisfaction degree evaluation of a mosaic warfare system to meet the problem of applicability, and although capability demands of equipment clusters for meeting tasks are theoretically guaranteed, due to lack of optimization and consideration of cluster strategies, real problems of poor cluster synergy, overhigh cluster running cost and the like can occur in actual combat, so that the obtained evaluation conclusion does not meet actual combat.

Disclosure of Invention

In view of the foregoing, an objective of the present application is to provide a method and related equipment for evaluating the capability requirement satisfaction of a mosaic warfare system, which are used for solving or partially solving the above technical problems.

Based on the above object, a first aspect of the present application provides a method for evaluating capability requirement satisfaction of a mosaic warfare system, including:

building a mosaic system super-network model according to the fight equipment of the mosaic system, wherein the mosaic system super-network model comprises: an equipment layer, a cluster layer, a task layer and a capability layer;

receiving a running resolution, determining a mission task according to the running resolution, decomposing the mission task, logically combing the decomposition result to obtain a task hierarchical decomposition structure, and deploying the task hierarchical decomposition structure in the task layer in a task activity network mode;

determining system combat capability meeting the mission task, constructing a capability requirement tree network based on the task hierarchical decomposition structure, and deploying the capability requirement tree network in the capability layer;

determining an optimal cluster grouping strategy corresponding to a mosaic warfare system according to a task activity network deployed in the task layer and the capability demand tree network deployed in the capability layer, and deploying the optimal cluster grouping strategy in the cluster layer;

And analyzing the demand satisfaction degree of the cluster performance index in the cluster layer on the capability layer, and carrying out upward aggregation of the demand satisfaction degree based on the capability demand tree network to obtain a capability demand satisfaction degree evaluation result of a mosaic war system.

A second aspect of the present application provides a mosaic warfare architecture capability requirement satisfaction evaluation device, comprising:

the model construction module is configured to construct a mosaic system super network model according to the combat equipment of the mosaic combat system, wherein the mosaic system super network model comprises: an equipment layer, a cluster layer, a task layer and a capability layer;

the task decomposition module is configured to receive the issued battle resolution, determine a mission task according to the battle resolution, decompose the mission task, logically comb the decomposition result to obtain a task hierarchical decomposition structure, and deploy the task hierarchical decomposition structure in the task layer in a task activity network mode;

a capability requirement analysis module configured to determine a system combat capability that satisfies the mission task, and construct a capability requirement tree network based on the task hierarchical decomposition structure, the capability requirement tree network being deployed in the capability layer;

The cluster strategy analysis module is configured to determine an optimal cluster grouping strategy corresponding to a mosaic warfare system according to the task activity network deployed in the task layer and the capability demand tree network deployed in the capability layer and deploy the optimal cluster grouping strategy in the cluster layer;

the demand satisfaction degree aggregation module is configured to analyze the demand satisfaction degree of the cluster performance indexes in the cluster layer on the capability layer, and conduct upward aggregation of the demand satisfaction degree based on the capability demand tree network to obtain a capability demand satisfaction degree evaluation result of a mosaic warfare system.

A third aspect of the present application proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the program.

A fourth aspect of the present application proposes a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of the first aspect.

From the above, it can be seen that the method and the related device for evaluating the satisfaction degree of the capability requirement of the mosaic battle system provided by the application take the actual task requirement of the mosaic battle system as traction, and perform evaluation through four steps of capability requirement analysis, mission task analysis, cluster strategy analysis and capability aggregation analysis. The method creatively introduces the optimization of the cluster grouping strategy into the system combat capability requirement satisfaction evaluation, and performs comprehensive optimization on the cluster grouping and task allocation strategy in aspects of cluster cooperative preference, subtask capability requirement satisfaction, cluster actuation cost and the like, so that the capability satisfaction evaluation conclusion is reasonable and reliable, the actual combat requirement is met, and compared with the existing capability satisfaction evaluation method facing the traditional combat system, the method is more suitable for the combat characteristics of mosaic combat.

Drawings

In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.

Fig. 1 is a flow chart of a method for evaluating the satisfaction of the capability requirement of the mosaic warfare system according to the embodiment of the application;

FIG. 2 is a schematic diagram of a framework of a method for evaluating the satisfaction of the capability requirements of a mosaic warfare system according to an embodiment of the present application;

FIG. 3 is a schematic diagram of a super network model of a mosaic warfare system according to an embodiment of the present application;

fig. 4 is a schematic diagram of a basic structure of a mosaic war system equipment layer network according to an embodiment of the present application;

fig. 5 is a schematic diagram of a hierarchical exploded structure of an IDEF0 of a battle task of a mosaic battle system according to an embodiment of the present application;

fig. 6 is a schematic diagram of a mosaic war system capability requirement tree network structure according to an embodiment of the present application;

fig. 7 is a schematic diagram of grouping and task allocation modes of a mosaic warfare system according to an embodiment of the present application;

FIG. 8 is a schematic diagram of four cases of interval performance indicators in collaborative preference evaluation according to an embodiment of the present application;

FIG. 9 is a schematic diagram of four common forms of task performance requirement satisfaction functions in accordance with embodiments of the present application;

FIG. 10 is a schematic diagram of a capacity requirement network adjacency matrix according to an embodiment of the present application;

FIG. 11 is an exploded view of an island control project of the mosaic war system according to an embodiment of the present application

Fig. 12 is a schematic diagram of a tree network of island capture control setting capability requirements of the mosaic war system according to an embodiment of the present application;

FIG. 13 is a schematic diagram of a top ranking 5 dry-beat cluster of integrated collaborative preference values in accordance with an embodiment of the present application;

FIG. 14 is a schematic diagram of a top 5 best-effort cluster of task capability requirement satisfaction ranks according to an embodiment of the present application;

fig. 15 is a schematic diagram of an analysis of the influence of the TOPSIS running cost weight on the system capacity requirement satisfaction evaluation value in the embodiment of the present application;

FIG. 16 is a schematic diagram illustrating an analysis of the influence of the TOPSIS subtask capability demand satisfaction and cluster collaborative preference weights on the system capability demand satisfaction in an embodiment of the present application;

fig. 17 is a schematic diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings.

It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.

How to evaluate the capability requirement satisfaction degree of a mosaic battle system for specific battle tasks is an important research topic for building the mosaic battle system and pushing the mosaic battle from a basic idea to practical application. The capability demand satisfaction degree evaluation is a thermal difficulty problem in the field of combat system evaluation research, the basic research thought is to construct a capability evaluation index system according to actual combat tasks, evaluate combat capabilities of the combat system in all aspects from equipment performance indexes, and obtain a final capability demand satisfaction degree evaluation conclusion by comparing with expected capability demands. However, the existing combat system capability demand satisfaction degree evaluation research is largely carried out on the traditional combat system, and the field is still lacking in the performance demand satisfaction degree evaluation research results oriented to the mosaic combat system.

Compared with the traditional combat system, the mosaic combat system is more complex in equipment composition and cooperative mode, and the main constituent of the mosaic combat system is small intelligent unmanned equipment with numerous quantity, huge scale and various functions. Limited by the singleness and specificity of single-package combat functions, mosaic combat systems have more complex cooperative relationships of equipment clusters in performing tasks, and the capability requirements of a simple combat task often require that multiple unmanned equipment with complementary functions be grouped into combat clusters to be met. Different cluster cooperation strategies have important influence on the cluster combat effect and the overall combat capability of a combat system, and further influence the system capability requirement satisfaction evaluation result, so that the cluster cooperation strategies are important factors to be considered in the mosaic combat system capability requirement satisfaction evaluation.

The scheme provides a task-oriented mosaic battle system battle capability requirement satisfaction evaluation method. The actual task demands of the mosaic battle system are used as traction, and assessment is carried out through four steps of capability demand analysis, mission task analysis, cluster strategy analysis and capability aggregation analysis. The method creatively introduces the optimization of the cluster grouping strategy into the system combat capability requirement satisfaction evaluation, and performs comprehensive optimization on the cluster grouping and task allocation strategy in aspects of cluster cooperative preference, subtask capability requirement satisfaction, cluster actuation cost and the like, so that the capability satisfaction evaluation conclusion is reasonable and reliable, the actual combat requirement is met, and compared with the existing capability satisfaction evaluation method facing the traditional combat system, the method is more suitable for the combat characteristics of mosaic combat.

The framework of the mosaic battle system capability demand satisfaction evaluation method is shown in fig. 2, and the evaluation process is divided into four steps, namely mission task analysis, capability demand analysis, cluster strategy analysis and demand satisfaction aggregation.

The assessment process takes mission task decomposition as a main basis, and a system capacity demand tree network is constructed based on a task decomposition structure. The cluster cooperation and task allocation strategy in the combat process is optimized by comprehensively considering the cluster cooperation tendency among intelligent armed devices and the capability requirement satisfaction degree of meta-tasks. And then, combining actual combat activities, evaluating the support degree of the combat clusters on the bottom layer capacity requirement indexes, and finally, aggregating upwards to form a system capacity requirement satisfaction evaluation conclusion. Because the cluster cooperation strategy is closely related to the overall operational capacity of the system, different influences of different cluster strategies on the evaluation result of the system operational capacity demand satisfaction degree are mainly analyzed in the research.

As shown in fig. 1, this embodiment proposes a method for evaluating the satisfaction of capability requirements of a mosaic warfare system, including:

step 101, building a mosaic system super-network model according to the fight equipment of the mosaic fight system, wherein the mosaic system super-network model comprises: an equipment layer, a cluster layer, a task layer and a capability layer.

To serve the capability demand satisfaction assessment, a mosaic warfare system super-network model is constructed, as shown in fig. 3. The model can assist in analyzing the mosaic combat mechanism, combing complex combat processes, task allocation modes and equipment cooperative modes in the mosaic combat process, and analyzing the essential characteristics of the mosaic combat system capability requirement satisfaction evaluation problem.

The layering mode of the method is in conformity with the assessment method framework, and the method comprises an equipment layer network, a cluster layer network, a task layer network and a capability layer network, wherein the equipment layer network describes the coordination and association relation among weapons and equipment in a mosaic battle system and is a modeling basis of a super network model, and the cluster layer network, the task layer network and the capability layer network respectively correspond to cluster strategy analysis, mission task analysis and capability requirement analysis processes in capability requirement satisfaction assessment. The model reflects the decomposition mapping relation from the system capacity requirement to the single-package performance index from top to bottom, and reflects the capacity requirement satisfaction degree aggregation process from the single-package performance index to the system capacity requirement from bottom to top.

In some embodiments, step 101 is specifically:

step 1011, constructing an equipment layer according to a cooperative relationship and an association relationship existing between the fight equipments of the mosaic fight system, wherein the node types of the equipment layer comprise: at least one of a command class, a reconnaissance class, a decision class, a hit class, and a guarantee class.

Equipment layer network G _e The synergy and association relationship existing between equipments in mosaic war system is described. Different from the traditional fight system, the mosaic fight system disperses fight functions such as command, reconnaissance, decision, strike, interference and the like on the function integrated platform to a plurality of small intelligent weaponry equipment, and each equipment can perform task bidding and cooperative combination according to fight task requirements so as to realize complex fight functions. Thus, the equipment layer network has significant heterogeneity. The node types of the equipment layer network comprise command class V _e ^C Class V of reconnaissance _e ^S Decision class V _e ^D Beating class V _e ^A Guarantee class V _e ^G And the specific meanings and functions are shown in Table 1. Equipment connecting edge E _e The combat cooperative relationship among the equipment when the combat task is executed is described, and the meanings of the cooperative relationship among different types of nodes are shown in table 2. The basic network structure of the equipment layer network is shown in fig. 4, where T represents a Lan Fangmu node. The mosaic combat system has highly complex OODA circulation (packet De circulation, observation Orientation DecisionAction), forms a wide area killing network with wide distribution, high redundancy and strong adaptability, changes the defect that the traditional combat mode depends on a single-chain combat ring, and can not be greatly influenced by the damage and failure of part of nodes.

TABLE 1 mosaic warfare System Equipment node types

TABLE 2 edge type between mosaic warfare System Equipment nodes

Step 1012, constructing a cluster layer according to the combat cluster composition and the cooperative relationship among clusters in the mosaic combat by the mosaic combat system.

Cluster layer network G _g The description of the combat cluster composition and the cooperative relationship among clusters in mosaic combat is an important embodiment of the mosaic combat system different from the traditional combat system. The equipment that has functional coupling or complementation relationships is limited by the singleness and specificity of the individual combat functions, and typically performs combat tasks in a manner that constitutes a combat cluster. The aim of constructing the cluster layer network is to assist in analyzing the natural mode of combined synergy in the mosaic war system and finding out a potential optimal combined synergy mode. A combat cluster is a combat co-team consisting of multiple intelligent weaponry pieces that are autonomous to a particular combat mission. The node generation in the cluster layer network represents a combat cluster, and has a corresponding relation with the equipment layer network node. The edges of the cluster-layer network represent the synergistic relationship between clusters. As the course of the battle advances, nodes and edges in the cluster layer network change as the task allocation and cluster grouping policies change.

Step 1013, constructing a task layer according to the logical relationship and the evolution process of the combat task under the task control of the mosaic combat system, setting the distribution relationship between the task layer and the cluster layer, and setting the corresponding relationship between the task layer and the capability layer.

Task layer network G _t The logical relationship and the evolution process of the combat task under the task command are described, the distribution relationship between the task activities and the combat clusters is described downwards, and the corresponding relationship between the task activities and the capability requirements is described upwards. The aim of constructing the task layer network is to assist the mosaic battle system in any one by decomposing the battle task, combing the logic relation between the task activitiesAnd modeling the dynamic collaboration and task allocation process under the transactional command. The task command is a core command mode of mosaic war, the main idea is to refer to the release and dispersion of control rights, the superior command officer takes charge of issuing a combat task, and the specific execution and completion modes of the task are submitted to combat power autonomous decision. Nodes of the task layer network represent combat activities, and edges represent logical relationships between combat activities. The application adopts IDEF0 technology (Icam Definition Method) to carry out task layer network modeling. The method adopts a graphical and structured form to establish a functional model of the activity flow, and clears the whole context of the system activity from the logic relation and the content, and decomposes the complex system flow into activity units with clear logic and clear functions from top to bottom.

Step 1014, constructing a capability requirement tree network according to the combat capability of combat equipment in the mosaic combat system, and completing construction of a super network model of the mosaic combat system, wherein nodes of the capability layer network comprise: at least one of capability requirements, sub-capabilities, meta-capabilities.

Capability layer network G _c The constituent structure of the capacity requirement is described in the form of a tree network, and the capacity requirement layering characteristic from the top-level combat requirement to the bottom-level capacity index is shown. The nodes of the capability layer network comprise three types of nodes including capability requirements, sub-capability, meta-capability indexes and the like, the top-layer capability requirements are decomposed into sub-capability layer by layer, and finally the sub-capability indexes which can be quantitatively measured are decomposed. The capability requirement is a top node of the tree network and corresponds to a top-level mission task. Sub-capabilities are intermediate nodes of the capability network, which are intermediate products of capability decomposition. The meta-capability index is the bottom node of the capability network and corresponds to the combat technical index or the performance index of the combat cluster. The edges in the capability layer network include two categories of capability composition and capability dependency. Capability composition describes the dependencies between higher-level capabilities and lower-level capabilities, and capability dependencies describe the associations and dependencies between peer-level capabilities.

The capability requirement satisfaction degree evaluation of the mosaic battle system is divided into four steps, namely, step 102 mission task analysis, step 103 capability requirement analysis, step 104 cluster strategy analysis and step 105 requirement satisfaction degree aggregation. The cluster strategy analysis and the demand satisfaction aggregation are important manifestations of a mosaic battle system in system capacity demand satisfaction evaluation research, which are different from a traditional battle system.

Step 102, receiving the issued battle resolution, determining a mission task according to the battle resolution, decomposing the mission task, logically combing the decomposition result to obtain a task hierarchical decomposition structure, and deploying the task hierarchical decomposition structure in the task layer in a task activity network mode.

The battle resolution is an overall strategic goal proposed for the battle system and is issued by the superior decision maker in a strategic intention and task list manner. Due to the singleness and specificity of the function of the equipment in the mosaic battle system, the mission tasks in the task list need to be cooperatively completed by the weapon equipment in a battle cluster mode. To determine cluster grouping and task allocation policies, the task list needs to be broken down into explicit task activities in detail.

In some embodiments, step 102 specifically includes:

and 1021, receiving the issued battle resolution, determining a mission task according to the battle resolution, decomposing the mission task to obtain a subtask, and decomposing the subtask to obtain a meta-task.

And determining a mission task according to the battle resolution, wherein the mission task is an ordered set of a series of interrelated battle actions which are executed by battle forces for achieving the preset battle purpose. In the mission task decomposition process, different levels of combat tasks are defined as follows:

definition [ mission task ] high-level strategic mission with totality and abstract property, which is proposed by the superior combat decision maker to the combat system, covers a plurality of subtasks with time, space and functional logic relations.

Definition [ subtasks ], intermediate products from the breakdown of mission tasks to meta-tasks, which can be done individually or cooperatively by the combat cluster, can be further broken down into subtasks or meta-tasks.

Definition [ meta-task ], a meta-task is the smallest unit of task that can be performed by equipment, also called a war activity, that cannot be broken down further.

Unlike traditional combat systems, mosaic combat is an intelligent combined combat scene, and combat tasks have higher complexity and layering due to the fact that the number of combat weapon equipment is large and the cooperative relationship is complex. The method adopts an IDEF0 hierarchical decomposition structure technology to realize layer-by-layer decomposition from mission tasks to meta-tasks and logic carding of task activities of various layers, as shown in fig. 5. In the task hierarchical decomposition structure, a mission task layer, a subtask layer and a meta task layer are respectively corresponding to an equipment system, a combat cluster and weaponry from top to bottom.

And 1021, performing activity logic combing according to the logic relationship among the task, the subtask and the meta-task which are obtained by decomposition to obtain a task hierarchical decomposition structure, and disposing the task hierarchical decomposition structure in the task layer in a task activity network mode.

Complex time sequence constraint and logic relation exist among meta-tasks, constraint relation exists among execution processes of different meta-tasks, and a certain limitation is also provided for equipment cluster grouping and task allocation strategies. The logical relationship between meta-tasks mainly comprises the following:

defining the sequence relation among the meta-tasks, wherein the execution of the follow-up meta-tasks is the necessary condition for the completion of the follow-up meta-tasks before the execution of the follow-up meta-tasks.

Definition (or logic) exists among parallel meta-tasks, and direct influence does not exist among meta-tasks with existence or logic relation, so that the execution condition of the following meta-tasks can be met after any one of the meta-tasks is completed.

Definition and logic exist among parallel meta-tasks, direct influence does not exist between meta-tasks with logic relations, and when all meta-operation tasks with logic relations are completed, the execution condition of the following meta-tasks can be met.

Taking fig. 5 as an example to illustrate the logical relationship among meta-tasks, the meta-task layer is a task network composed of 5 meta-tasks. Wherein T is ₃₁ And T is ₃₂ There is a logical relationship withT which can be satisfied only if both of them execute completion ₃₃ Execution conditions of (2); t (T) ₃₄ And (3) with _T35 There is a logical relationship between them, and the two can meet the execution condition of the following task after completing one of them.

Step 103, determining the system combat capability meeting the mission task, constructing a capability requirement tree network based on the task hierarchical decomposition structure, and deploying the capability requirement tree network in the capability layer.

The purpose of the capability requirement analysis is to decompose the overall capability requirement of the system level into specific war technical index requirements of a cluster or equipment level, and lay a foundation for the follow-up development of cluster collaborative strategy optimization and capability satisfaction degree aggregation work. Capacity demand analysis can be divided into two specific steps: and (3) providing system capability requirements and constructing a capability requirement network, wherein the result is displayed in the capability layer of the mosaic system super network model in the form of a tree network.

The system capacity requirement is the overall capacity requirement of the superior decision maker to the battle system, is issued in a capacity willingness manner, and corresponds to battle resolution in the mission analysis stage. The system capacity requirements are abstract and macroscopic, and the system capacity requirements are deconstructed by performing satisfaction analysis on the capacity requirements.

In some embodiments, step 103 specifically includes:

step 1031, constructing a capability requirement tree network according to the hierarchical relationship of the task activity network of the task hierarchical decomposition structure in the task layer.

Step 1032, determining a corresponding weight value for each capability index corresponding to each layer of nodes in the capability requirement tree network.

And 1033, establishing corresponding edge weights for the subordinate relations and the dependent relations between the combat capabilities in the capability requirement tree network by adopting an analytic hierarchy process or a Delphi process, and deploying the finally obtained capability requirement tree network in the capability layer.

Capability decomposition studies of equipment systems are typically conducted in the form of capability structure trees. Due to the limitation of the tree structure, only the subordinate relations among different-layer capabilities are considered, and the interaction among the same-layer capabilities is ignored. Because the equipment cooperation in the mosaic war system has stronger compactness and coupling, complex association relations and dependency relations exist among the same-layer capacity indexes, neglecting the same-layer relations can lead to errors of capacity demand satisfaction evaluation results. Thus, the present application is a capability requirement decomposition of the development of the mosaic system in the form of a tree network, as shown in fig. 6. The capability requirement network is based on a tree structure, and besides the capability subordinate relations among layers, the consideration of the association relation of the capabilities of the same layer is enhanced. In a hierarchical manner, the hierarchy of the capacity demand network corresponds to the equipment architecture hierarchy and mission task hierarchy. In order to follow-up trace back the equipment combat technical index to the satisfaction degree of the system capacity requirement, the capacity requirement network is required to correspond to actual combat mission activities during construction and is mapped to specific combat clusters and weapon equipment. Then, a weight value of each capability index needs to be further determined. At present, related researches generally adopt an analytic hierarchy process or a Delphi method, and the expert experience knowledge is combined to give the edge weight to the dependency relationship and the dependency relationship between the combat capabilities in the capability requirement network.

Step 104, determining an optimal cluster grouping strategy corresponding to a mosaic warfare system according to the task activity network deployed in the task layer and the capability demand tree network deployed in the capability layer, and deploying the optimal cluster grouping strategy in the cluster layer.

Because the combat function of a single package is single and specific, the combat cluster cooperation is a basic combat mode of mosaic combat, and the execution of a subtask is usually completed by cooperation of a plurality of functionally complementary devices. Autonomous cluster grouping is an important aspect of a mosaic battle system, which is different from a traditional equipment system, and the equipment system develops cluster grouping decisions according to the evolution of battlefield situations and the demands of task activities. The cluster grouping strategy influences the execution progress and the completion degree of the combat task, and further determines the overall capacity level and the capacity demand satisfaction degree of the system. Therefore, cluster policy analysis is an indispensable key step in the demand satisfaction evaluation of the mosaic warfare system.

Step 104 is to search an optimal cluster grouping strategy of the equipment system around the mosaic warfare system cluster grouping optimization model according to the fight task capability requirement, and specifically illustrates theoretical emphasis such as cluster cooperation preference, meta-task capability requirement satisfaction, cluster running cost and the like.

In some embodiments, step 104 specifically includes:

step 1041, constructing a cluster marshalling optimization model of the mosaic war system, wherein the specific formula is as follows:

wherein F is _g Cluster collaborative preference for cluster g, < ->

Satisfaction of capability requirement for meta-task t, C _g And obtaining a cluster starting cost value for the sum of the equipment costs in the cluster g, wherein n is the number of clusters, and m is the number of meta-tasks.

Step 1042, determining a candidate cluster, and calculating the meta-task demand satisfaction degree, the cluster cooperation preference degree and the cluster output action cost of the grouping strategy corresponding to the candidate cluster according to the cluster grouping optimization model as three factors.

Step 1043, determining the combat requirement of the candidate cluster, determining the relative weight ratio of the three factors according to the combat requirement, and aggregating the three factor scores by using a TOPSIS comprehensive scoring method to obtain the comprehensive score value of the grouping strategy corresponding to the candidate cluster.

Step 1044, determining an optimal cluster grouping strategy corresponding to the mosaic war system according to the comprehensive score value of the grouping strategy corresponding to the candidate cluster, and deploying the optimal cluster grouping strategy in the cluster layer.

In the scheme, the combat cluster grouping of the mosaic combat is essentially the task allocation problem of the intelligent equipment system under the task command. The manner of task allocation in mosaic warfare is generally understood as a "consumer-provider" model. The combat task is a Consumer, and specific combat index capability requirements are set forth for the equipment system. The equipment system is a 'provider', and each equipment performs task bidding by combining the capability requirement of the task and the capability attribute of the equipment. Among all the equipment participating in bidding, the command equipment selects the best weapon equipment combination in combination with the actual situation, and groups the weapon equipment into a combat cluster to execute meta-tasks, as shown in fig. 7.

Cluster grouping strategy optimization of a mosaic battle system is a multi-objective combination optimization problem, and aims to improve meta-task demand satisfaction of the cluster grouping strategy under the limitation of battle rules and the constraint of battlefield situations

And the cooperative preference degree F of the equipment cluster is used for reducing the cluster running cost C. Accordingly, a cluster grouping optimization model of the mosaic warfare system equipment is constructed.

And after the meta-task demand satisfaction degree, the cluster cooperation preference degree and the cluster running cost of the candidate cluster grouping strategy are calculated, giving out the relative weight ratio of three factors according to the actual combat demand, and aggregating the three factor scores by utilizing a TOPSIS comprehensive evaluation method to finally obtain the comprehensive score value of the cluster grouping strategy. The TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) is a common comprehensive evaluation method, and is commonly used for solving the problems of multi-index and multi-scheme evaluation and sequencing in the technical field of engineering, and judging the advantages and disadvantages of schemes according to the relative closeness of each evaluation object to a positive ideal scheme and a negative ideal scheme.

The variables and definitions involved in the overall process of step 104 are shown in table 3, and the key theory in the optimization model will be described in detail below.

TABLE 3 Cluster marshalling optimization notation

In some embodiments, the process of calculating the cluster co-preference includes:

the inherent cluster coordination preference among equipment is the key research content in the field of intelligent equipment cluster grouping research. The cluster collaborative preference analysis is independent of the combat scene and the combat process, and mainly starts from certain equipment performance indexes and functional attributes, analyzes the matching degree of the performance indexes among the equipment, and further analyzes the inherent tendency of the equipment to form the combat cluster. The performance index data of the equipment are commonly in discrete class and continuous class, and the following cluster collaborative preference calculation method is adopted according to different data types.

Step A1, determining cluster cooperation preference among equipment by combining adaptation data of the war skill indexes among the equipment in response to determining that the performance indexes of the equipment in the candidate clusters are discrete war skill indexes, wherein the specific formula is as follows:

wherein F is _eiej To be equipped with e _i And equipment e _j P is a war technical index.

Discrete war indexes such as enumeration type parameters (such as communication link type), boolean type parameters (such as whether a reconnaissance function is provided) and the like are required to be combined with the adaptation condition of the war indexes to evaluate the cluster coordination preference among equipment. If the two devices can meet the cooperative requirement on a certain index, the cooperative preference value is considered to be 1, otherwise, the cooperative preference value is 0, and the cooperative preference value is shown in the formula.

And step A2, in response to determining that the performance index of the equipment in the candidate cluster is a continuous index, calculating the fitting degree of the index requirement value and the actual index value, and determining the cluster coordination preference among the equipment according to the fitting degree.

When the cluster cooperation preference value is calculated aiming at the continuous index, the real value convergence rate is generally calculated by the real value type performance index, and the interval coverage rate is generally calculated by the interval type performance index.

When the performance index demand is of a real-valued type, the closer the actual parameter value of the equipment relatively approaches the cooperative demand value, the greater the likelihood of forming a combat cluster between the equipment is considered. The convergence rate calculation method is as follows.

When the performance index requirement is of the interval type, four situations may be encountered when calculating the cooperative preference of the interval type performance index according to the different position relations between the actual interval and the required interval of the equipment performance index, as shown in fig. 8. Except the case (1) in the figure, the cluster cooperation preference value can be calculated as the ratio of the overlapping length of the actual interval of the performance index and the demand interval to the demand length, and the calculation formula is shown as follows.

After the cooperative preference values of the candidate clusters under each performance index are calculated respectively, the cooperative preference values under each index are aggregated by utilizing a TOPSIS comprehensive evaluation method, and the comprehensive cluster cooperative preference values of the candidate clusters are obtained.

In some embodiments, the meta-task demand satisfaction includes: cluster performance index aggregation and meta-task capability requirement functions.

The equipment cluster grouping strategy of the mosaic battle system not only needs to consider inherent cluster cooperation preference among equipment, but also needs to consider the satisfaction degree of cluster battle capacity on meta-task capacity requirements, so as to ensure that the task capacity requirements are met to the greatest extent. The following describes the computing method of the meeting degree of the meta-task capability requirement from two aspects of a cluster performance index aggregation mode and a meta-task capability requirement function.

The determining process of the cluster performance index aggregation mode comprises the following steps:

and step B1, determining the main body of the executed meta-task as a candidate cluster.

And step B2, determining the cluster performance index by adopting an aggregation mode of Boolean logic operation in response to determining that the performance index of the equipment in the candidate cluster is a discrete performance index.

And step B3, determining the cluster performance index by adopting an aggregation mode of calculating statistical values in response to determining that the performance index of the equipment in the candidate cluster is the continuous war technical index.

In the mosaic battle system, a battle cluster is a main body for executing meta-tasks, and the capability attribute of the cluster is an aggregation result of performance indexes in the interior of the cluster. Thus, cluster performance index aggregation calculation is the first step in meta-task capability demand satisfaction analysis. And according to different data types of the performance indexes, adopting different aggregation modes of cluster performance indexes.

The discrete performance indexes adopt a Boolean logic operation aggregation mode, and the cluster capability attribute value is obtained by performing AND or equal logic operation on the discrete indexes provided in the cluster, for example, the important condition that the combat cluster has the reconnaissance capability is that equipment individuals with the reconnaissance capability exist in the cluster. For equipment clusters g= { e ₁ ,e ₂ ,…,e _n The performance index of the cluster may be calculated according to the following equation.

g(k _p )＝e ₁ (k _p )∧e ₂ (k _p )∧...∧e _n (k _p )；g(k _p )＝e ₁ (k _p )∨e ₂ (k _p )∨...∨e _n (k _p )

The continuous war technical index adopts an aggregation mode of calculating statistical values, comprising a maximum value, a minimum value, an average value, a summation value and the like, for example, the reconnaissance distance of a combat cluster is the maximum value of reconnaissance distances of all equipment in the cluster. For equipment clusters g= { e ₁ ,e ₂ ,…,e _n And the warfare index g (k) _p ) Can be calculated according to the following formula.

Some of the more specific indicators take the form of probability distribution values, random values, etc. For example, the projectile spread of a single hit arrangement is a probability density function that is related to the target distance, and a joint probability density function needs to be calculated when calculating the clustered projectile spreads.

The meta-task capability requirement function includes at least one of: a large-scale demand function, a small-scale demand function, a central demand function, and an interval demand function.

Meta task capability demand function

Describing the capability requirement condition of the meta-task on the cluster performance index, and classifying task capability requirement functions into four types: the trend toward larger, smaller, center and compartment types, as shown in fig. 9, is described below in connection with examples in mosaic war for various capacity demand functions.

Large demand function: the larger the performance index is within a certain range, the better, and the satisfaction degree is 1 after reaching a certain threshold. The index value is generally required to be not less than a certain threshold value, otherwise, the demand satisfaction is 0. For example, a remote hit mission requires a range of an unmanned bomber, and if the range of the unmanned bomber can support completion of the mission and return smoothly, the performance index is considered to basically meet the mission capacity requirement, and on this basis, the larger the range, the higher the degree of satisfaction of the equipment to the mission requirement is considered.

Miniaturization-oriented demand function: the smaller the performance index is within a certain range, the better, and the satisfaction degree is 1 after the performance index is smaller than a certain threshold value. The index value is generally required to be not greater than a certain threshold value, otherwise, the demand satisfaction is 0. For example, certain reconnaissance tasks have higher timeliness requirements on destination information, the smaller and better the time delay of an unmanned reconnaissance machine for transmitting the destination information to the striking equipment is, and if the time delay of the information is larger than a certain preset value, the destination information is considered to be invalid.

Center type demand function: the highest degree of capability requirement satisfaction at a certain value of the performance index, the better the closer to the point, the worse the further from the point. For example, in an electronic interference task, an electromagnetic wave emitted by an interfering machine has the best interference effect on an adversary at a specific frequency, and the interference effect is weakened when the electromagnetic wave deviates from the frequency.

Interval type demand function: the performance index has the highest satisfaction degree of the energy demand in a certain interval, and the closer to the interval is, the better the performance index is, and the further from the interval is, the worse the performance index is. For example, when the task of precisely breaking armor work is performed, the static armor breaking depth of the armor breaking projectile carried by the unmanned bomber is required to be within a certain range, the armor breaking effect cannot be achieved due to insufficient armor breaking depth, and surrounding buildings are damaged due to excessive armor breaking depth.

The cluster actuation cost determination process comprises the following steps:

the cluster performs tasks with a certain cost. For a combat cluster containing n devices g= { e ₁ ,e ₂ ,…,e _n The cost of the operation is the sum of the cost of the equipment inside the cluster, and the cost is shown in the following formula.

In addition to the above optimization objectives, the cluster grouping strategy of the mosaic combat equipment system is also constrained by real-time battlefield situations such as equipment occupancy states, equipment geographic positions and the like, and neglecting these constraints can lead to the fact that the cluster grouping strategy cannot be applied to combat practice. In the same period, one piece of equipment participates in performing at most one meta-task. Due to the complex logical sequential relationship between combat tasks, there may be overlap in execution time periods between certain tasks. In determining cluster groupings and task allocation policies, conflicts between these tasks need to be avoided. Taking fig. 7 as an example, the device 2 has the capability D required for the metatask 3, but the metatask 3 cannot be executed because it is already occupied by the metatask 2 which is performed simultaneously with the metatask 3. In addition, the requirement of actual combat on task timeliness is generally high, and when the cluster is grouped and tasks are distributed, the geographical position of the equipment after completing the preceding tasks needs to be considered, so that whether the equipment can reach the next task location in a designated time is judged.

And 105, analyzing the demand satisfaction degree of the cluster performance index in the cluster layer on the capability layer, and carrying out upward aggregation of the demand satisfaction degree based on the capability demand tree network to obtain a capability demand satisfaction degree evaluation result of a mosaic battle system.

Assuming that the capability requirement network can be divided into n layers according to node levels, wherein the topmost layer is a system capability requirement layer, the middle (n-2) layer is a sub-capability requirement layer, the bottommost layer is a performance index requirement layer, and the number of nodes contained in each layer is { m }, respectively ₁ ,m ₂ ,…,m _n }. The adjacent matrix of the internal nodes in each layer from top to bottom is { A }, respectively ₁ ,A ₂ ,…,A _n }, wherein A _i For the number of lines m _i The element values of the square matrix of the (a) represent whether the nodes of the same layer are connected or not and the weight size. Setting the adjacent matrix between the nodes of the ith layer and the (i-1) th layer as A _(i,i-1) Is the number of lines of m _r The column number is m _r-1 The element values of the matrix of (a) represent whether or not the nodes of different layers are connected and the weight size. Taking the three-layer capacity demand network shown in FIG. 10 as an example, the intra-layer adjacency matrix A is shown in the following formula ₂ ,A ₃ And an inter-layer adjacency matrix A _(2,1) ,A _(3,2) Is a value of (a).

Let the input vector of the ith layer in the capacity demand network be P _i The output vector is Q _i The input vector is from the aggregation of the output values of the nodes of the next layer, and the output vector is the determined output value of the layer after the same-layer propagation is completed.

The capability requirement aggregation approach on a capability requirement tree network can be understood as a two-stage process of first propagating horizontally across layers and then propagating longitudinally across layers. In the co-layer propagation process, the layer output vector is equal to the dot product of the layer input vector and the in-layer adjacency matrix, and the co-layer propagation mode of the ith layer is shown as follows.

Q _i ＝P _i A _i

And after the same-layer transmission is completed, cross-layer transmission is carried out, the output vector is transmitted to the upper layer network according to the inter-layer adjacency matrix, and the cross-layer transmission mode from the ith layer to the i-1 layer is shown in the following formula.

P _i-1 ＝Q _i A _(i，i-1) ＝P _i A _i A _(i，i-1)

For the bottom capability requirement network, the input vector is the satisfaction value of the bottom performance index, namely P _m ＝[v ₁ ,v ₂ ,…,v _nm ]. According to the capability requirement satisfaction degree propagation formula, the bottom performance index layer P is deduced _m To top-level system capacity demand satisfaction evaluation value Q ₁ The aggregate calculation formula of (2) is shown as follows.

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Based on the same inventive concept, a specific description of the method for evaluating the capability requirement satisfaction of the mosaic warfare system of the present application is made below in one specific embodiment.

Taking island control fight of a mosaic fight system as a case, evaluating the capability requirement satisfaction degree of the mosaic fight system on the designed background, and carrying out validity verification on the capability requirement satisfaction degree evaluation method of the mosaic fight system.

Step 201, setting a background

The intelligent aerial formation combat is used as a main combat background, the red party combat equipment system is a mosaic combat system which takes an unmanned combat aircraft as a main body, and the blue party deployment combat effort is a traditional ground air defense anti-pilot combat system. The red party dispatch intelligent aerial formation carries out island reef capture control combat on the island occupied by the blue party, and combat resolution is to destroy infrastructure and living forces of temporary command posts, power stations, radars, air-defense missiles, hangars and the like of the island deployed on the island.

The red combat effort is a mosaic combat system mainly composed of air combat effort, and specific equipment configurations are shown in table 4. The main facilities of blue party deployment on the D island are shown in table 5.

TABLE 4 Red Fang Masai g warfare force of the warfare System

TABLE 5 deployment facility and combat effort on blue square island

The combat zone in this embodiment is an open sea area near the D island, and is divided into a non-countermeasure zone, a weak countermeasure zone, and a strong countermeasure zone in order according to the distance according to the capability level of the blue party reconnaissance early warning system and the air defense counterguidance system. For the area closer to the island D, the reconnaissance early warning capability and the air defense reverse conduction capability of the blue party are stronger, and the blue party does not have the early warning and air defense capability for the targets in the unopposed area; the method has the advantages that the method has early warning capability on non-stealth targets in weak countermeasure areas but cannot intercept refusal; the method has the advantages of early warning and air defense capability on non-stealth targets and part of stealth targets in the strong countermeasure area. In the actual fight process, unmanned plane such as red side conveyer, early warning machine and the like are located in the non-contrast area, and unmanned plane and stealth plane such as unmanned plane and bomber develop direct fight in the strong contrast area.

The ability demand satisfaction of the red Fang Masai g battle system in this example for island control tasks was evaluated. Because the small and medium unmanned combat equipment in the red combat system has single combat function, complex task-oriented cluster cooperative relationships exist among the equipment for completing complex combat activities, and the functional coupling among the equipment is strong, the method is suitable for the mosaic combat system capability demand satisfaction degree assessment method.

Step 202, experimental data

The performance index data of the unmanned aerial vehicle in the design are random numbers generated within a certain range, and the value range and the basic information of each index are shown in an attached table. The value of the running cost of the equipment is related to the performance parameters of the equipment, and the higher the comprehensive performance is, the higher the running cost of the equipment is, and the value is a real number in an interval [0,100 ].

Step 203, task decomposition and description

An exploded view of the task of the present proposed system for controlling island capture in the medium-red Fang Masai g battle is shown in fig. 11. The entire combat process can be divided into three main phases: the method comprises a global sensing stage, a matrix unfolding stage and an unmanned main warfare stage. The main combat tasks of each stage are briefly described below, and the capacity demand satisfaction assessment hereinafter is mainly performed for the unmanned main combat stage.

(1) Global sensing phase

The red party has a man-made early warning machine and a conveyor (carrying an unmanned aerial vehicle) to fly against a blue party unopposed region of the island sea area, the conveyor releases an unmanned reconnaissance machine, the unmanned aerial vehicle realizes autonomous formation in a weak fight region, and weapons and infrastructure are deployed from multiple directions outside the island reef to develop remote joint reconnaissance on the blue party. And after the task is completed, the corresponding equipment is retracted to the unopposed area, and situation information is reported to the early warning machine.

(2) Array position unfolding stage

The early warning machine carries out combat mission decomposition according to battlefield situations and combat resolution, and issues decision-making unmanned aerial vehicles. The latter guides other unmanned aerial vehicles to develop task bidding according to task demands and self-ability attributes, then autonomously generates task allocation strategies and cluster grouping strategies according to bidding conditions, and directs unmanned equipment to conduct cluster grouping in a weak countermeasure zone.

(3) Unmanned main battle stage

Unmanned aerial vehicle enters a strong countermeasure area to carry out combat activities, and the stage comprises 4 percussion subtasks, and targets are a blue square radar system, an air defense counterguidance system, a temporary command post and other infrastructures on an island in sequence. According to different target attributes and numbers, the sub-tasks and the meta-tasks which are respectively subordinate to each sub-task are different in number. In the unmanned main battle stage, the unmanned bomber mainly executes the battle task, and the unmanned reconnaissance aircraft, the unmanned electronic battle aircraft and the unmanned fighter aircraft execute the fight guarantee, and execute the approaching reconnaissance, the electronic interference and the accompanying flight protection guarantee activities respectively.

Step 204, capability requirement network

The step mainly carries out capability requirement analysis on the unmanned main warfare stage in the embodiment, and the corresponding mosaic warfare system capability requirement tree network is shown in fig. 12. The vertical solid lines in the tree network represent hierarchical dependencies of capabilities and the horizontal dashed lines represent influencing relationships of capabilities at the same level. The unmanned main battle stage comprises subtasks such as target reconnaissance, cluster marshalling, accurate bombing, accompanying flight shielding, damage evaluation and the like, and the corresponding relation between the performance index requirements of the network bottom layer and the battle subtasks is expressed in different colors. For the weight vector of the capability requirement network, the weight vector of the system capability requirement to system capability requirement aggregate when capability is aggregated up is defined as ω1= {0.2,0.1,0.4,0.1,0.2}. When the rest layers are aggregated upwards, the weights among all child nodes subordinate to the father node are equal. The capability impact weight between peer nodes is defined as 0.1.

Step 205, unmanned equipment cluster collaborative preference calculation

The cluster cooperation preference value among unmanned equipment is calculated according to the basic performance index similarity of the equipment, and comprises cruising speed, cruising altitude, combat range, duration, communication link and the like. And then comprehensively analyzing the similarity of the indexes according to a TOPSIS method, and finally determining the comprehensive scores of the cooperative preference among the equipment. And for the combat clusters with the equipment number larger than 2, calculating the cooperative preference values of the equipment in the clusters, and then calculating the average value of the preference values as the cooperative preference analysis result of the clusters. Taking the dry-run cluster related to the accurate bomber task as an example, the collaborative preference analysis result is shown, and fig. 13 shows a dry-run cluster equipment combination collaborative scheme with the comprehensive collaborative preference value of 5 in the top ranking.

Fig. 13 contains 5 clusters, each cluster contains 1 each of reconnaissance unmanned aerial vehicle, interference unmanned aerial vehicle and striking unmanned aerial vehicle, the serial numbers in the legend are the serial numbers of three unmanned aerial vehicles respectively, for example, "scheme 1: [12,31,78]" represents a dry-detection and striking operation cluster consisting of reconnaissance unmanned aerial vehicle number 12, interference unmanned aerial vehicle number 31 and striking unmanned aerial vehicle number 78. In the graph, each coordinate axis represents a cluster preference comprehensive score value and 5 basic performance scores, each score is located in a [0,1] interval, and the higher the score is, the more outstanding the cluster is represented in the basic performance index. It can be seen that under the condition of similar comprehensive scores, each cluster scheme has stronger comprehensive cooperative preference, but the capability level in the aspect of basic performance has larger difference, and the difference is also reflected on other performance indexes.

Step 206, subtask capability demand satisfaction calculation for unmanned equipment cluster

Firstly, determining the specific capacity requirement of each task according to a task decomposition structure, and then calculating the satisfaction degree of the unmanned combat cluster scheme on the subtask capacity requirement according to the requirement satisfaction degree function of each task. The formulas of the task capacity demand satisfaction evaluation functions of several classes employed in the case analysis are shown below, where f ₁ (x)，f ₂ (x)，f ₃ (x)，f ₄ (x) And the demand satisfaction degree functions of the large-scale type, the small-scale type, the regional type and the central type are respectively corresponding.

And determining the task capacity demand function type and the function parameter value according to the task characteristics. And in the equipment clusters with higher collaborative preference values, calculating the satisfaction degree of the performance indexes of each cluster on the task demands. FIG. 14 illustrates a top-of-rank 5 dry-hit unmanned cluster solution for accurate bombing subtask capability demand satisfaction, with representative indicators selected to demonstrate the capability demand satisfaction level of each solution for a task. The task capacity requirements of the five cluster schemes are similar in average satisfaction, but part of the schemes have obvious short boards in the aspects of performance such as navigational speed, reconnaissance range, damage effect and the like.

Step 206, TOPSIS-based combat task cluster allocation

On the basis of the cooperative preference analysis of the equipment cluster and the subtask capability demand satisfaction analysis, the cluster running cost is combined, and the comprehensive evaluation score value of the cluster scheme is calculated for each combat subtask based on the TOPSIS method. And selecting the cluster with the top ranking of 10 as a subtask recommended cluster set and as a basis for system capacity requirement satisfaction analysis.

Taking the cluster with the top score of 10 as an example, table 6 shows the accurate bombing subtask cluster distribution result based on the TOPSIS method when three indexes of cluster cooperation preference, subtask capability demand satisfaction and cluster running cost value are set to be equal weights.

TABLE 6 precise bombing subtask Cluster distribution results based on TOPSIS method

Step 207, system capacity demand satisfaction analysis

Based on the combat task cluster allocation result and the aggregate performance index of the equipment cluster, the capability requirement tree network structure and the weight vector are combined to realize the upward aggregation of the capability requirement satisfaction evaluation, and the upward aggregation is used as a system capability requirement satisfaction evaluation result. When the cluster cooperation preference, the subtask capability requirement satisfaction degree and the weight setting of the cluster starting cost value are equal, the capability requirement satisfaction degree of the targeted mosaic war system on the island reef capture control war scene is 0.754.

In the subtask cluster allocation process based on the TOPSIS method, three indexes of cluster cooperation preference, subtask capability demand satisfaction and cluster running cost value respectively represent different directives of a decision maker on a task allocation strategy, and the three indexes respectively exert different influences on a task cluster allocation result by self weight, so that the capability demand satisfaction of a system on a combat task is influenced.

And when the satisfaction degree weight of the cooperative preference of the fixed cluster and the demand satisfaction degree weight of the subtask capability is 0.5, analyzing the influence of the cluster cost factors on the system combat capability by adjusting the cost weight in the task cluster allocation. As shown in fig. 15, when the cluster running cost weight increases, that is, the limit of the decision maker on the combat cost becomes more strict, the subtask capability requirement satisfaction and the cluster cooperation preference all have a sliding trend, which indicates that the overall combat capability and combat cooperation of the system are reduced, and the falling trend of the former is more remarkable than the latter. Along with the requirement, the overall system capacity requirement satisfaction degree of the mosaic war system in the island reef capture control scene is gradually reduced. The system capacity demand satisfaction degree can be improved by reducing the weight of the cluster running cost, but when the weight of the cluster running cost is 0.1, the system capacity demand satisfaction degree can not be greatly improved by continuously loosening the cost limit, and reaches the platform stage.

When the fixed cluster running cost weight is 0.5, the influence of the subtask capability requirement satisfaction and the cluster cooperative preference on the system capability requirement satisfaction is analyzed by adjusting the weight of the subtask capability requirement satisfaction and the cluster cooperative preference in task cluster distribution. As shown in fig. 16, when the sum of the two weights is fixed, the system capability requirement satisfaction gradually increases when the cluster collaborative preference weight gradually decreases and the subtask capability requirement satisfaction weight gradually increases. However, a phenomenon that occurs simultaneously with this is a deterioration in the degree of synergy preference of the combat clusters, i.e. a deterioration in the rationality and reliability of the distribution of combat tasks and grouping of clusters of the equipment hierarchy. In actual combat, if the basic performance indexes of the equipment in the cluster are not matched, the combat effect of the equipment cluster may be greatly affected.

In practice, when the cluster cooperative preference weight is set to 0 and the subtask capability requirement satisfaction weight is set to 1, that is, the system capability requirement satisfaction evaluation process only focuses on the equipment capability level, but does not focus on the cluster cooperative rationality among the equipment, and the obtained evaluation conclusion is equal to the traditional capability requirement satisfaction method. Although the system capacity demand satisfaction evaluation value obtained under the condition is higher, the method ignores the inherent cooperative combination rule among unmanned equipment in the mosaic battle system, and the conclusion is that the method is established on the cluster grouping and task allocation strategy which lacks the cooperative performance and does not accord with the actual battle characteristics of the mosaic battle, so that the method is not suitable for evaluating the capacity demand satisfaction of the mosaic battle system.

In summary, the embodiment provides a framework of a method for evaluating the capability requirement satisfaction degree of a mosaic battle system against a mosaic battle background based on intelligent unmanned equipment, and the evaluation process comprises four steps of capability requirement analysis, mission task analysis, battle cluster analysis, capability aggregation analysis and the like. Because the functions of weaponry in the mosaic battle system are single, a large number of cluster cooperation phenomena exist in the battle process, and the cluster cooperation strategy influences the overall battle capacity level of the mosaic battle system to a great extent. In this regard, traditional equipment system-oriented capability satisfaction assessment methods are not applicable to mosaic warfare systems, and are particularly characterized by the lack of consideration and optimization of equipment cluster strategies and task allocation strategies in such traditional methods. The mosaic warfare system capacity demand satisfaction evaluation method provided by the embodiment takes actual combat tasks as guidance, comprehensive optimization is performed on the aspects of cluster cooperation preference, subtask capacity demand satisfaction, cluster running cost and the like of the equipment system, and the obtained evaluation conclusion is fit with the actual combat characteristics of the mosaic warfare system, so that the method is more pertinent and reliable compared with the traditional evaluation method.

In the embodiment, the island control scene of the mosaic war system is taken as a wanted case, and the feasibility and the effectiveness of the method for evaluating the capability requirement satisfaction degree of the mosaic war system are verified. Experimental results show that the evaluation conclusion obtained by the method of the embodiment is established on the basis of a reasonable equipment cluster cooperation strategy and a task allocation strategy by introducing the cluster cooperation preference analysis into the capability satisfaction evaluation process. Compared with the traditional method, the method of the embodiment is more fit with the actual combat background of the mosaic combat, and has advantages in the aspect of evaluating the reliability of the conclusion.

Meanwhile, the embodiment analyzes the influence of three factors of cluster cooperation preference, subtask capability requirement satisfaction and cluster running cost on the system capability requirement satisfaction evaluation conclusion. The cluster cooperation and task allocation strategy with rough cost can promote the rising of the meeting degree of subtask capacity demands and the cluster cooperation preference level, so that the system capacity demand meeting degree evaluation value is improved, but when the cluster cost weight is reduced to 0.1, the continuous reduction of the weight value can not greatly improve the system capacity demand meeting degree; when the cluster running cost weight is fixed, the cluster cooperative preference and the subtask capability requirement satisfaction have opposite influence on the system capability requirement satisfaction, when the subtask capability requirement satisfaction is set to be high in the cluster strategy, the evaluation value of the system capability requirement satisfaction is improved, but the evaluation conclusion is based on the cluster strategy with lower cluster cooperative preference, so that the obtained evaluation value is high in deficiency, and the expected combat effect is difficult to achieve in actual combat. Under actual combat and training scenes, combat decision-making personnel need to reasonably measure and distribute weights of three elements by combining specific combat demands, so that reasonable capability satisfaction evaluation conclusion can be obtained.

It should be noted that, the method of the embodiments of the present application may be performed by a single device, for example, a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of embodiments of the present application, and the devices may interact with each other to complete the methods.

It should be noted that some embodiments of the present application are described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Based on the same inventive concept, the application also provides a mosaic warfare system capability requirement satisfaction evaluation device corresponding to the method of any embodiment.

The mosaic warfare system capacity demand satisfaction evaluation device comprises:

For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.

The device of the foregoing embodiment is configured to implement the corresponding method for evaluating the satisfaction of the capability requirement of the mosaic war system in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.

Based on the same inventive concept, the application also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for evaluating the capability requirement satisfaction degree of the mosaic war system according to any embodiment when executing the program.

Fig. 17 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1810, a memory 1820, an input/output interface 1830, a communication interface 1840, and a bus 1850. Wherein the processor 1810, the memory 1820, the input/output interface 1830, and the communication interface 1840 enable communication connection therebetween within the device via the bus 1850.

The processor 1810 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.

The Memory 1820 may be implemented in the form of ROM (Read Only Memory), RAM (RandomAccess Memory ), a static storage device, a dynamic storage device, or the like. The memory 1820 may store an operating system and other application programs, and when the embodiments of the present disclosure are implemented in software or firmware, the associated program code is stored in the memory 1820 and executed upon invocation by the processor 1810.

The input/output interface 1830 is used to connect with the input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown in the figure) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.

The communication interface 1840 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).

Bus 1850 includes a path to transfer information between components of the device (e.g., processor 1810, memory 1820, input/output interface 1830, and communication interface 1840).

It is noted that although the above-described devices illustrate only the processor 1810, the memory 1820, the input/output interface 1830, the communication interface 1840, and the bus 1850, the device may include other components necessary to achieve proper operation in an implementation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.

The electronic device of the foregoing embodiment is configured to implement the corresponding method for evaluating the satisfaction of the capability requirement of the mosaic war system in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.

Based on the same inventive concept, corresponding to any of the above embodiments of the method, the present application further provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the mosaic warfare architecture capability requirement satisfaction evaluation method according to any of the above embodiments.

The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.

The storage medium of the foregoing embodiment stores computer instructions for causing the computer to execute the method for evaluating the capability requirement satisfaction degree of the mosaic warfare system according to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.

Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the present application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.

Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform on which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.

While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements and/or the like which are within the spirit and principles of the embodiments are intended to be included within the scope of the present application.

Claims

1. The method for evaluating the capability requirement satisfaction of the mosaic warfare system is characterized by comprising the following steps of:

analyzing the demand satisfaction degree of the cluster performance indexes in the cluster layer on the capability layer, and carrying out upward aggregation of the demand satisfaction degree based on the capability demand tree network to obtain a capability demand satisfaction degree evaluation result of a mosaic war system;

the construction of the super network model of the mosaic system according to the fight participating equipment of the mosaic fight system specifically comprises the following steps:

constructing an equipment layer according to the cooperative relationship and the association relationship existing between the fight equipment of the mosaic fight system, wherein the node type of the equipment layer comprises: at least one of a command class, a reconnaissance class, a decision class, a hit class, and a guarantee class;

constructing a cluster layer according to the combat cluster composition of the mosaic combat system in the mosaic combat and the cooperative relationship among clusters;

Constructing a task layer according to the logical relationship and the evolution process of the fight task under the task command of the mosaic fight system, setting the distribution relationship between the task layer and the cluster layer, and setting the corresponding relationship between the task layer and the capability layer;

building a capability requirement tree network according to the combat capability of combat equipment in the mosaic combat system, and completing building of a super network model of the mosaic combat system, wherein nodes of the capability layer network comprise: at least one of capability requirements, sub-capabilities, meta-capabilities;

the determining an optimal cluster grouping strategy corresponding to a mosaic warfare system according to the task activity network deployed in the task layer and the capability requirement tree network deployed in the capability layer and deploying the optimal cluster grouping strategy in the cluster layer comprises the following steps:

the cluster marshalling optimization model of the mosaic war system is constructed, and the specific formula is as follows:

wherein F is _g Cluster collaborative preference for cluster g, < ->

Satisfaction of capability requirement for meta-task t, C _g The cluster starting cost value obtained by taking and summing the equipment costs in the cluster g is obtained, n is the number of clusters, and m is the number of meta-tasks;

determining candidate clusters, and calculating the meta-task demand satisfaction degree, the cluster cooperation preference degree and the cluster starting cost of a grouping strategy corresponding to the candidate clusters according to the cluster grouping optimization model to serve as three factors;

Determining the combat requirement of the candidate cluster, determining the relative weight ratio of the three factors according to the combat requirement, and aggregating the three factor scores by using a TOPSIS comprehensive scoring method to obtain the comprehensive score value of the grouping strategy corresponding to the candidate cluster;

determining an optimal cluster grouping strategy corresponding to a mosaic battle system according to the comprehensive score value of the grouping strategy corresponding to the candidate cluster, and deploying the optimal cluster grouping strategy in the cluster layer;

the calculation process of the cluster cooperative preference degree comprises the following steps:

in response to determining that the performance index of the equipment in the candidate cluster is a discrete war skill index, determining cluster coordination preference among the equipment by combining the adaptation data of the war skill index among the equipment, wherein the specific formula is as follows:

wherein F is _eiej To be equipped with e _i And equipment e _j P is a war technical index;

and in response to determining that the performance index of the equipment in the candidate cluster is a continuous index, calculating the degree of agreement between the index requirement value and the actual index value, and determining the cluster cooperation preference among the equipment according to the degree of agreement.

2. The method according to claim 1, wherein the receiving the assigned battle resolution, determining a mission task according to the battle resolution, decomposing the mission task, and logically combing the decomposition result to obtain a task hierarchical decomposition structure, wherein the task hierarchical decomposition structure is deployed in the task layer in a form of a task activity network, and specifically comprises:

Receiving a next combat decision, determining a mission task according to the combat decision, decomposing the mission task to obtain a subtask, and decomposing the subtask to obtain a meta-task;

and performing activity logic carding according to the logic relation among the task mission task, the subtask and the meta-task which are obtained through decomposition to obtain a task hierarchical decomposition structure, and disposing the task hierarchical decomposition structure in the task layer in a task activity network mode.

3. The method of claim 2, wherein the determining the system combat capabilities that meet the mission tasks and constructing a capability requirement tree network based on the task hierarchical decomposition structure, deploying the capability requirement tree network in the capability layer, comprises:

constructing a capacity demand tree network according to the hierarchical relation of the task activity network of the task hierarchical decomposition structure in the task layer;

determining corresponding weight values for all capacity indexes corresponding to all layers of nodes in the capacity demand tree network;

and establishing corresponding edge weights for the subordinate relations and the dependent relations between the operational capabilities in the capability requirement tree network by adopting an analytic hierarchy process or a Delphi process, and deploying the finally obtained capability requirement tree network in the capability layer.

4. The method of claim 1, wherein the meta-task demand satisfaction comprises: cluster performance index aggregation mode and meta-task capability requirement function;

determining the main body of the executed meta-task as a candidate cluster;

determining cluster performance indexes by adopting a Boolean logic operation aggregation mode in response to determining that the performance indexes of the equipment in the candidate clusters are discrete performance indexes;

determining cluster performance indexes by adopting an aggregation mode of calculating statistical values in response to determining that the performance indexes of the equipment in the candidate clusters are continuous war technical indexes;

5. A mosaic warfare architecture capability demand satisfaction evaluation device, comprising:

the demand satisfaction degree aggregation module is configured to analyze the demand satisfaction degree of the cluster performance indexes in the cluster layer on the capability layer, and conduct upward aggregation of the demand satisfaction degree based on the capability demand tree network to obtain a capability demand satisfaction degree evaluation result of a mosaic warfare system;

The model building module is further configured to:

the cluster policy analysis module is further configured to:

wherein F is _g Cluster collaborative preference for cluster g, < - >

6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when the program is executed by the processor.

7. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 4.