CN114862152A - Target importance evaluation method based on complex network - Google Patents
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Abstract
A target importance evaluation method based on a complex network comprises the following steps: step 1: modeling the operational capacity, namely realizing the quantification of the target operational capacity according to the target characteristics; step 2: modeling a network topological structure of a combat system, namely realizing the quantification of the action status of a target in a network according to the network topological structure of the combat; and step 3: the method comprises the following steps of (1) target combat capability model expansion, namely evaluating a target according to a combat intention, and converting the attack intention into the capability of the target needing to be attacked and the action position of the target in a combat network; and 4, step 4: giving a specific evaluation score, namely setting target constraint and solving; the defects of insufficient effectiveness and quantization degree of the method for evaluating the combat targets in the prior art are effectively overcome by combining with another structure.
Description
Technical Field
The invention relates to the technical field of target importance evaluation, in particular to a target importance evaluation method based on a complex network.
Background
The networked combat system is the association of various weapons, equipment, facilities, forces and the like in a joint combat. The different elements of the battle are usually dynamically regrouped according to the battle mission. The method comprises the steps of dividing various targets in a combat system into layers according to specific rules, quantifying the target capacity according to classification and grading, determining key nodes and key parts in a combat network, and selecting, sequencing and evaluating the combat targets, and is an important basis for executing combat actions such as accurate striking.
How to find key targets of enemies to attack from a dynamic combat system is an important research topic for improving combat efficiency, so that important targets are attacked, the combat system is paralyzed, and the attack is a dynamic combat system. The traditional evaluation method is to perform qualitative and quantitative analysis according to expert experience, experimental data and combat cases and establish a threat degree model of a target so as to realize value evaluation of the target, but the general effectiveness and the quantitative degree of the evaluation method are insufficient.
Disclosure of Invention
In order to solve the problems, the invention provides a target importance evaluation method based on a complex network, which effectively overcomes the defect that the effectiveness and the quantization degree of the evaluation method for the combat target in the prior art are insufficient.
In order to overcome the defects in the prior art, the invention provides a solution of a target importance evaluation method based on a complex network, which comprises the following steps:
a target importance evaluation method based on a complex network comprises the following steps:
step 1: modeling the operational capacity, namely realizing the quantification of the target operational capacity according to the target characteristics;
step 2: modeling a network topological structure of a combat system, namely realizing the quantification of the action status of a target in a network according to the network topological structure of the combat;
and step 3: the method comprises the following steps of (1) target combat capability model expansion, namely evaluating a target according to a combat intention, and converting the attack intention into the capability of the target needing to be attacked and the action position of the target in a combat network;
and 4, step 4: and giving a specific evaluation score, namely setting target constraints and solving.
Further, the step 1 specifically includes:
the method is characterized in that the target system hierarchy is divided by adopting an analytic hierarchy process, and specifically, a series of single targets with the same or similar properties are divided into a plurality of system hierarchies according to the rules of the functional characteristics, the status function and the position distribution of the targets.
Further, the step 2 specifically includes:
for each target node, the action in the battle network system can be described by network parameters such as degree centrality, recenterness, aggregation coefficient, mesocentrality and K-Shell core degree.
Further, the centrality of degree D Ci The calculation formula (2) is shown in the following formula (1):
k i representing the number of the existing edges connected with a node i, wherein i is a positive integer, and the node i is the ith node;
n-1 represents the number of nodes except node i, and N identifies the number of all nodes.
Further, the recenterness C Ci The calculation formula is shown in the following formula (2):
n-1 represents the number of nodes other than node i;
d vi representing the average shortest distance of a node.
Further, the mesocentrality B Ci The calculation formula (2) is shown in the following formula (3):
g st represents the number of shortest paths connecting s and t;
after normalization, formula (4) can be obtained:
further, the K-Shell nuclear degree is K-Shell2 nuclear degree, and the K-Shell2 nuclear degree is calculated in a layer-by-layer peeling mode: firstly, directly stripping edge nodes, setting a variable total and recording the total as 1 because the minimum degree of a network is 1 and the core degree of the stripped node K-Shell2 is 1; then, the second layer is stripped, the minimum degree is still 1, the total is recorded as 1+1 as 2, namely the core degree of the stripped node K-Shell2 is 2; stripping a third layer, wherein the minimum degree is 2, the total is recorded to be 2+ 2-4, namely the core degree of the stripped node K-Shell2 is 4; finally, the fourth layer can be completely stripped, the minimum degree is 3, the total is recorded to be 4+3 to be 7, and the remaining node K-Shell2 has the core degree of 7.
Further, the step 3 specifically includes:
and expanding the ability use degree centrality, the recenterness and the mesocentrality of the target in the fighting network and the K-Shell2 core degree by combining with a quantification model of the fighting ability, and obtaining an augmentation matrix M + of the ability of the target by combining with the ability matrix M of the target.
Further, the step 4 specifically includes:
modeling is performed based on demand constraints, the target striking number is set to be x, and the quantization capacity of each target is expressed as a row vector M k The fighting intent is expressed as a column vector W, F ═ M k W is the quantitative score obtained by the evaluation of the kth target, and then the maximum value of the sum of the evaluation scores of the x capacity indexes is taken from all targets according to the formula (5):
the first x targets with the highest evaluation score of the overall capability index are solved, and k is a positive integer.
The invention has the beneficial effects that:
according to the invention, a series of single targets with the same or similar properties are divided into a plurality of system layers according to the rules of the functional characteristics, the status function and the position distribution of the targets, so that the characteristics and the operation rule of the battle targets can be better researched and mastered. In addition, by focusing on the joint combat command under the information condition, the K-Shell2 nuclear degree is defined according to the network topological property, and a quantification method of the network node combat capability is designed, so that the capability of the target per se is considered, and the position and the action of the target in the combat network are also considered. In conjunction with the intent of the campaign, the goals are effectively evaluated. When the fighting intention changes, the weighted value of the ability changes, and the ranking of the targets also changes. In practical application, the relation between targets can change dynamically, and the calculation complexity is greatly increased along with the increase of the number of network nodes, the number of target attacks and the calculation amount of the overall performance index. The defects that the effectiveness and the quantization degree of the method for evaluating the combat targets in the prior art are insufficient are effectively overcome.
Drawings
Fig. 1 is a block diagram of a target fighting ability quantification model of the present invention.
FIG. 2 is a schematic diagram of a detailed table of airport target capabilities of the present invention.
FIG. 3 is a schematic diagram of a K-shell kernel algorithm.
FIG. 4 is a schematic diagram of the K-shell2 kernel algorithm of the present invention.
FIG. 5 is a diagram of a target capability quantification extension of the present invention.
FIG. 6 is a schematic diagram of a B-party target architecture distribution architecture of an embodiment of the present invention.
FIG. 7 is a B-party target global capability value list of an embodiment of the present invention.
Fig. 8 is a table of capability item weight relationship assignment values according to an embodiment of the present invention.
FIG. 9 is a B-party target quantitative score table of an embodiment of the present invention.
FIG. 10 is a comparison of the target importance assessment of the present invention.
Detailed Description
The invention will be further described with reference to the following figures and examples.
As shown in fig. 1 to 10, a target importance evaluation method based on a complex network includes:
step 1: modeling the operational capacity, namely realizing the quantification of the target operational capacity according to the target characteristics;
step 2: modeling a network topological structure of a combat system, namely realizing the quantification of the action status of a target in a network according to the network topological structure of the combat;
and step 3: the method comprises the following steps of (1) target combat capability model expansion, namely evaluating a target according to a combat intention, and converting the attack intention into the capability of the target needing to be attacked and the action position of the target in a combat network;
and 4, step 4: and giving a specific evaluation score, namely setting target constraints and solving.
Further, the step 1 specifically includes:
on the basis of the characteristic research of a combat system under a complex network condition, various nodes with complex functions and relations in the combat system are analyzed, and the target system is constructed through the means of target characteristic selection, system hierarchical division, network structure association and the like. The method is characterized in that a plurality of system levels are formed by dividing a series of single targets with the same or similar properties according to the rules of functional characteristics, status functions and position distribution of the targets, so that target characteristics and operation rules can be better researched and mastered. For example, based on the functional characteristics of the target, the target system can be generally divided into: reconnaissance early warning system, command control system, air defense back-leading system, firepower striking system, information attacking and defending system and comprehensive support system.
For example, capacity analysis is performed based on a constructed target system, and the target capacity is quantified by dividing the six capacities of reconnaissance early warning, command control, air defense and counter guidance, firepower striking, information attack and defense and comprehensive guarantee by adopting an analytic hierarchy process, so that a target operational capacity quantification model shown in fig. 1 is established.
Meanwhile, the operational target is composed of sub-targets, and the operational capacity of the operational target is composed of the operational capacity of the sub-targets. Taking an airport as an example, the target is composed of sub-targets such as a radar station, a communication station, a fighter plane, a runway and the like. The target decomposition shows that the target has the capabilities of air reconnaissance, air percussion, command control, communication and the like as shown in fig. 2.
Carrying out quantitative assignment on the target capability refinement table to obtain a capability vector V ═ m of the target 1 ,m 2 ,…,m n ]Thus, the capability matrix M of all targets in the battle network is finally obtained:
wherein m is 11 、m 21 ...m p1 Is the element of m1, m 12 、m 22 ...m p2 Is the element of m2, m 1n 、m 2n ...m pn I.e. the elements of M2, p and n are positive integers, each element in the capability vector V is the capability of a corresponding single object, and each capability of each object constitutes an element of the capability matrix M.
For example, the relationship between goals and capabilities can be as shown in Table 1:
TABLE 1
Further, the step 2 specifically includes:
the network of the battle system can also be represented by (V, E), wherein V represents a node and represents each battle object in the battle network, and E represents a directed edge or an undirected edge between nodes, which not only describes the relationship of command, guarantee, communication, cooperation and the like between the nodes, but also can express the dynamic relationship between the flexible battle objects.
For each target node, the action in the battle network system can be described by network parameters such as degree centrality, recenterness, aggregation coefficient, mesocentrality and K-Shell core degree.
Further, degree is the most basic concept in single node attributes, and the degree of a node is the number of other nodes connected with the node, which is a simple but very important value in network analysis. The degree of centrality D Ci The calculation formula (2) is shown in the following formula (1):
k i representing the number of the existing edges connected with a node i, i is a positive integer, the node i is the ith node, D Ci The degree centrality of the ci-th node is obtained, and the value of ci is equal to i;
n-1 represents the number of nodes except node i, and N identifies the number of all nodes.
Further, the distance between two points in the battle network is defined as the number of edges included in the shortest path connecting the two points. The average path length of a combat network refers to the average distance of all node pairs in the combat network, which reflects the degree of separation between nodes in the combat network and the global characteristics of the network. The near centrality C Ci The calculation formula is shown in the following formula (2):
n-1 represents the number of nodes other than node i;
d vi representing the average shortest distance of a node.
Furthermore, the betweenness includes node betweenness and edge betweenness, the node betweenness refers to the proportion of the quantity of all shortest paths in the network passing through the node, and the edge betweenness refers to all shortest paths in the networkBy the number of edges. The betweenness reflects the action and the influence of the corresponding node or edge in the whole network, and has strong practical significance. For example, in a traffic network, the probability of congestion of a road with a high betweenness is high; in a communication network, the channel with higher betweenness has the highest utilization rate; in the power network, the transmission lines and nodes with high betweenness are easy to be dangerous. The mesocentrality B Ci The calculation formula (2) is shown in the following formula (3):
g st represents the number of shortest paths connecting s and t;
after normalization, formula (4) can be obtained:
furthermore, Kitsak in 2010 proposes to apply a K-kernel decomposition algorithm in a complex network, and finds that a K-kernel decomposition method based on network topology can obtain an evaluation index, namely a K-shell kernel index, which describes node importance more accurately than degree centrality and betweenness centrality. The K-shell decomposition method provides a coarser granularity division of node importance, and the basic idea is as shown in fig. 2, wherein nodes in a network are divided into three layers through K-shell decomposition, and ks corresponds to 1, 2 and 3 respectively. The specific operation is as follows: as with shell stripping, the Kshell value of an edge node is first defined to be 1, and then enters the core of the network layer by layer internally. Firstly, stripping all nodes with the network middle degree of 1, removing edges of the nodes, then checking whether the nodes with the degree of 1 still exist in the rest nodes, if so, continuing to strip the nodes until the degrees of the rest nodes are all larger than 1, and the K-shell values of the stripped nodes are 1, namely the nodes are all in the layer with the ks value of 1; and next, sequentially stripping nodes and connecting edges with the stripping degrees less than or equal to k (k is an integer and k is more than or equal to 2) until all the nodes have corresponding ks values. The K-Shell nuclear power is K-Shell2 nuclear power, and as shown in FIG. 4, the K-Shell2 nuclear power is calculated in a layer-by-layer peeling mode: firstly, directly stripping edge nodes, setting a variable total and recording the total as 1 because the minimum degree of a network is 1 and the core degree of the stripped node K-Shell2 is 1; then, the second layer is stripped, the minimum degree is still 1, the total is recorded as 1+1 as 2, namely the core degree of the stripped node K-Shell2 is 2; stripping a third layer, wherein the minimum degree is 2, the total is recorded to be 2+ 2-4, namely the core degree of the stripped node K-Shell2 is 4; finally the fourth layer can be stripped off completely with a minimum of 3, with a record total of 4+3 of 7, i.e. the remaining node K-Shell2 has a core degree of 7.
Further, the step 3 specifically includes:
and expanding the ability use degree centrality, the recenterness and the mesocentrality of the target in the fighting network and the K-Shell2 core degree by combining with a quantification model of the fighting ability, and obtaining an augmentation matrix M + of the ability of the target by combining with the ability matrix M of the target.
The augmentation matrix M + for obtaining the capability of the target is to add the capability and the function of the battle network on the basis of the capability matrix M of the target.
An example of an augmentation matrix M + for the capabilities of the target is shown in Table 2:
TABLE 2
Further, the step 4 specifically includes:
the number of targets that can be hit per action is limited and the goal of the battle is to maximize the decline in the capacity of the battle system. Modeling is performed based on the demand constraint, the target striking number is set to be x, and the quantization capability of each target is expressed as a row vector M k With the intention of the battle being represented as a columnVector W, F ═ M k W is the quantitative score obtained by the evaluation of the kth target, and then the maximum value of the sum of the evaluation scores of the x capacity indexes is taken from all targets according to the formula (5):
w expresses the weight value occupied by each capability. For example, the air detection capacity accounts for 0.1, the sea detection capacity accounts for 0.2, …, the centrality accounts for 0.1, the KShell2 core degree accounts for 0.1, and the sum of the capacity value multiplied by the weight value is the score of a target.
The first x targets with the highest evaluation score of the overall capability index are solved, and k is a positive integer.
The invention is further illustrated by the following specific examples:
the party A needs to hit the target with the air defense and anti-pilot capability of the party B, and the target distribution of the party B is assumed as shown in FIG. 6, wherein the No. 1-4 targets are early warning nodes, the No. 5-8 targets are command control nodes, the No. 9-14 targets are fire nodes, and the No. 15-24 targets are communication nodes.
Determining the target list capacity value of the B party by a quantitative assignment method according to the target classification characteristics and the role status of the fighting network; calculating to obtain a network topological capability value of a target according to a combat network topological structure, wherein the network topological capability value specifically comprises a degree centrality, a near centrality, a betweenness centrality and a KShell2 index; the combined capability indicators are shown in FIG. 7 as a list of overall capability values for the targets.
The relationship of the capability weight according to the operational intention is shown in FIG. 8.
The fighting intention vector W is constructed according to the capability item weight relationship assignment table as [1, 1, 1, 1, 0, 0, 0, 0.1], the augmentation matrix M is constructed according to the target overall capability value list, and the quantitative scoring result of the target can be obtained through matrix operation Score as MW, as shown in fig. 9.
Under the guidance of a system breaking idea, key targets are selected to be precious but not too much, and key nodes of a hostile combat system are selected to give key strikes on the premise that striking cost is limited on the principle that links of enemies from command nodes to combat units and from sensors to transmitters are broken, so that the purposes of breaking links at points and breaking net faces are achieved. According to the score ranking, the targets of the first 6 hits are: 8,14, 13, 15, 19, 20. As can be seen, since the command ability of No. 8 is 2, the grade is the highest, and the evaluation is 1 st; no. 14 has the strongest air combat capability, and the evaluation score is 2; no. 13 air combat capability is inferior, and the evaluation is divided into No. 3; nodes No. 15, 19, 20 are in important positions of the battle network, and rank top in the strike evaluation.
As can be seen from fig. 10, the hierarchical analysis method gives an evaluation only based on the operational capability of the node itself, and does not consider the action of the rematerials system. When the target capability values are close, it is difficult to distinguish. Nor can targets be evaluated from the point of view of destroying the tactical system.
By focusing on the combined combat command under the information condition, the K-Shell2 nuclear degree is defined according to the network topology property, and a quantification method of the network node combat capability is designed, so that the capability of the target per se is considered, and the position and the action of the target in the combat network are also considered. In conjunction with the intent of the campaign, the goals are effectively evaluated. When the fighting intention changes, the weighted value of the ability changes, and the ranking of the targets also changes. In practical application, the relation between targets can change dynamically, and the calculation complexity is greatly increased along with the increase of the number of network nodes, the number of target attacks and the calculation amount of the overall performance index.
In modern war, when selecting target, not only the capability of target itself but also the role of target in battle network system should be considered. Based on a hierarchical analysis method, the invention provides a quantitative method for hierarchical classification of the capability of each target under a combat system; defining K-Shell2 core degree and establishing an importance degree evaluation model of a battlefield target based on a complex network theory; and solving key nodes of a combat system by taking the maximum hitting target capability as a constraint condition, and providing theoretical guidance and calculation support for target analysis and selection.
The present invention has been described above in an illustrative manner by way of embodiments, and it will be apparent to those skilled in the art that the present disclosure is not limited to the embodiments described above, and various changes, modifications and substitutions can be made without departing from the scope of the present invention.
Claims (9)
1. A target importance evaluation method based on a complex network is characterized by comprising the following steps:
step 1: modeling the operational capacity, namely realizing the quantification of the target operational capacity according to the target characteristics;
step 2: modeling a network topological structure of a combat system, namely realizing the quantification of the action status of a target in a network according to the network topological structure of the combat;
and step 3: the method comprises the following steps of (1) target combat capability model expansion, namely evaluating a target according to a combat intention, and converting the attack intention into the capability of the target needing to be attacked and the action position of the target in a combat network;
and 4, step 4: and giving a specific evaluation score, namely setting target constraints and solving.
2. The target importance evaluation method based on the complex network according to claim 1, wherein the step 1 specifically comprises:
the method is characterized in that the target system hierarchy is divided by adopting an analytic hierarchy process, and specifically, a series of single targets with the same or similar properties are divided into a plurality of system hierarchies according to the rules of the functional characteristics, the status function and the position distribution of the targets.
3. The target importance evaluation method based on the complex network according to claim 1, wherein the step 2 specifically comprises:
for each target node, the action in the battle network system can be described by network parameters such as degree centrality, recenterness, aggregation coefficient, mesocentrality and K-Shell core degree.
4. The method according to claim 1, wherein the degree-centrality D is a central value Ci The calculation formula (2) is shown in the following formula (1):
k i representing the number of the existing edges connected with a node i, wherein the i is a positive integer, and the node i is the ith node;
n-1 represents the number of nodes except node i, and N identifies the number of all nodes.
5. The method of claim 1, wherein the near-centrality C is a measure of the importance of the target based on the complex network Ci The calculation formula is shown in the following formula (2):
n-1 represents the number of nodes other than node i;
d vi representing the average shortest distance of a node.
6. The method according to claim 1, wherein the betweenness B is a measure of importance of the target based on the complex network Ci The calculation formula (2) is shown in the following formula (3):
g st represents the number of shortest paths connecting s and t;
after normalization, formula (4) can be obtained:
7. the target importance degree evaluation method based on the complex network as claimed in claim 1, wherein the K-Shell core degree is K-Shell2 core degree, and the K-Shell2 core degree is calculated by layer-by-layer peeling: firstly, directly stripping edge nodes, setting a variable total and recording the total as 1 because the minimum degree of a network is 1 and the core degree of the stripped node K-Shell2 is 1; then, the second layer is stripped, the minimum degree is still 1, the total is recorded as 1+1 as 2, namely the core degree of the stripped node K-Shell2 is 2; stripping a third layer, wherein the minimum degree is 2, the total is recorded to be 2+ 2-4, namely the core degree of the stripped node K-Shell2 is 4; finally, the fourth layer can be completely stripped, the minimum degree is 3, the total is recorded to be 4+3 to be 7, and the remaining node K-Shell2 has the core degree of 7.
8. The target importance evaluation method based on the complex network according to claim 1, wherein the step 3 specifically comprises:
and expanding the ability use degree centrality, the recenterness and the mesocentrality of the target in the fighting network and the K-Shell2 core degree by combining with a quantification model of the fighting ability, and obtaining an augmentation matrix M + of the ability of the target by combining with the ability matrix M of the target.
9. The target importance evaluation method based on the complex network according to claim 1, wherein the step 4 specifically comprises:
modeling is carried out based on requirement constraint, the number of target striking is set to be x, and the quantification capability of each target is expressed as a row vector M k The fighting intent is expressed as a column vector W, F ═ M k W is the quantitative score obtained by the evaluation of the kth target, and then the maximum value of the sum of the evaluation scores of the x capacity indexes is taken from all targets according to the formula (5):
the first x targets with the highest evaluation score of the overall capability index are solved, and k is a positive integer.
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| CN115659162A (en) * | 2022-09-15 | 2023-01-31 | 云南财经大学 | Method, system and equipment for extracting features in radar radiation source signal pulse |
| CN115659162B (en) * | 2022-09-15 | 2023-10-03 | 云南财经大学 | Method, system and equipment for extracting intra-pulse characteristics of radar radiation source signals |
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