Distributed air defense and anti-guide killing network construction and node value evaluation method

Abstract

The invention discloses a distributed air defense and antimissile killing network construction and node value evaluation method, and the method comprises the steps: carrying out the quantitative analysis of the node topological structure and performance index characteristics of an air defense and antimissile killing network super network model composed of a reconnaissance subnetwork, a command and control subnetwork and a firepower subnetwork, and combining the position and connection relation of nodes in a combat system; according to the method, quantitative calculation of node indexes is achieved, the importance of the indexes, the correlation degree between the indexes and the closeness degree of a scoring result and an ideal solution are comprehensively considered through improved weighted expert scoring and an entropy-grey correlation-TOPSIS algorithm, and therefore a more reliable overall effectiveness evaluation result of the killing network is obtained. The overall efficiency reduction degree of the killing network before and after node deletion is compared through a node deletion method, the value importance degree of the nodes is evaluated, and quantitative analysis of the value of the nodes of the air defense and anti-guide killing network is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of data management, and particularly relates to a method for constructing a distributed air defense and antimissile kill net and evaluating the value of nodes. Background Art

[0002] As an emerging combat mode, distributed combat plays an increasingly important role in modern warfare. As an important part of the distributed combat system, the effectiveness and survivability of the air defense and antimissile kill net directly affect the effectiveness of the entire combat system. In recent years, in the research on the modeling of the air defense and antimissile kill net system and the evaluation of node value in related fields by domestic and foreign scholars, there has been a trend of diversification and in-depth development. In terms of the construction of the kill net model.

[0003] There are some limitations in the existing research results: 1) The functional connections and interaction mechanisms between individual unit networks are not fully considered during model construction; 2) The attributes of node evaluation indicators are relatively single, only considering topological indicators such as the degree and betweenness of nodes, or only considering node performance indicators, without fully combining them; 3) Existing research mostly focuses on the local characteristics of nodes and their influence on adjacent nodes, while less evaluating the value and role of nodes in the entire system. Therefore, proposing a method for evaluating node value based on hypernetwork theory is of great significance for improving the combat effectiveness of the air defense and antimissile kill net. Summary of the Invention

[0004] In order to solve the technical problems in the background art, the present invention aims to provide a method for constructing a distributed air defense and antimissile kill net and evaluating the value of nodes. The present invention introduces hypernetwork theory, constructs a hypernetwork model reflecting the complex network characteristics of heterogeneous nodes and multiple links in the distributed air defense and antimissile kill net, and at the same time uses improved weighted expert scoring to suppress the irrational preferences of experts in scoring, maintaining the objective fairness of data performance index scoring and favoring fact maximization. By combining the entropy weight method, the grey relational analysis method and the TOPSIS method, giving full play to their respective advantages, comprehensively considering the importance of indicators, the degree of association between indicators, and the closeness of the scoring results to the ideal solution, so as to obtain more reliable results. Finally, the node deletion method is used to measure the value importance of nodes by comparing the degree of decline in the overall effectiveness of the hypernetwork before and after deleting nodes, realizing the quantitative analysis of the value of nodes in the air defense and antimissile kill net.

[0005] In order to solve the technical problems, the technical solution of the present invention is:

[0006] A method for constructing a distributed air defense and antimissile kill net and evaluating the value of nodes, comprising the following steps:

[0007] S1: Construct an air defense and anti-missile kill network super network model consisting of a reconnaissance subnet, a command and control subnet, and a firepower subnet; Construct an air defense and anti-missile kill network super network model H, which is divided into a reconnaissance subnet S, a command and control subnet C, and a firepower subnet A according to different functions and responsibilities, then H = S ∪ C ∪ A. Each subnet consists of a node set V = {v1, v2, …, v n} and edge set E = {e1,e2,…,e n}, the node set is divided into reconnaissance nodes V S 、Command node V C and Firepower Node V A , that is: V = (V S ,V C, V A ). The edge set E is divided into E S 、E C and E A , and the interaction between different types of nodes builds the connection between subnetworks. That is: E H =E S ∪E C ∪E A ∪E SC ∪E CA ∪E AS . Figure 1 A schematic diagram showing the construction of an air defense and anti-missile kill network super network model.

[0008] S2: Establish a node value assessment system. The node value assessment method is mainly divided into four steps.

[0009] The first step is to calculate the basic contribution value NR of each sub-node by combining the node topology metric and performance indicator relationship. i ;

[0010] The second step is to calculate the basic contribution value NR of all sub-nodes in the same sub-network. i The sum is the performance index score PM of each subnet i ;

[0011] The third step is to calculate the performance index scores of the reconnaissance subnet, command subnet and firepower subnet PM i The sum can give the overall efficiency CE of the super network;

[0012] The fourth step is to delete the node to be evaluated through the node deletion method to calculate the value importance SV of the node system. Figure 2 This is the node value assessment flow chart.

[0013] Further, the step S1 specifically includes:

[0014] S1.1: Construct a reconnaissance subnet model. The reconnaissance subnet can be described as (V S ,E S) It undertakes the functions of collecting, processing, and analyzing battlefield information. Reconnaissance node V S represents detection equipment nodes including ground radars, satellite receiving stations, early warning aircraft, sonars, etc. E S represents the information transmission paths and cooperation relationships between different reconnaissance nodes, and represents the actual communication links and information sharing paths between nodes.

[0015] S1.2: Construct a command and control subnet, which can be described as (V C , E C ), reflecting the battlefield command, control, and coordination efficiency, which can be characterized by the command decision-making ability, that is, the ability of the command node to schedule and issue instructions to different combat units and resources. Command node V C includes nodes such as command centers at all levels, communication networks, and data processing centers, and is responsible for formulating combat plans, making decisions, and commanding the actions of troops. E C represents the communication links and command relationships between command and control nodes, carrying the transmission of command orders, battlefield situation information, and decision support data, and reflecting the hierarchical and collaborative nature of the command structure.

[0016] S1.3: Construct a firepower subnet, which can be described as (V A , E A ), reflecting the firepower strike effectiveness and response speed on the battlefield, which can be described by the firepower strike ability, that is, the strike accuracy and coverage of the firepower node on different targets. Firepower node V A represents interception equipment nodes including ground air defense and anti-missile weapon systems, laser weapons, etc. E A represents key functions such as information sharing, coordinated strike, decision feedback, and fire support between firepower units.

[0017] S1.4: Construction of the inter-relationship between subnets. The reconnaissance subnet is a platform for battlefield information transmission and processing, sharing and exchanging intelligence data for different nodes of the command and control subnet. This inter-relationship between the reconnaissance subnet and the command and control subnet can be mathematically described as E SC .

[0018] Furthermore, as the support and core of the super network architecture, the command and control subnet ensures the coordinated operation of the entire network. It not only receives intelligence information from the reconnaissance subnet but also is responsible for transmitting decision instructions to the firepower subnet. This inter-relationship between the reconnaissance subnet, the command and control subnet, and the firepower subnet can be described by the edge sets E SC and E CA .

[0019] Furthermore, the firepower subnet receives the instructions and decisions of the command and control subnet and executes the firepower strike mission. The command node in the command and control subnet provides the firepower subnet with target information, strike timing, etc. The firepower subnet will feedback the strike effect and battlefield changes to the command and control subnet. This mutual relationship between the command and control subnet and the firepower subnet can be described by E CA for description.

[0020] Furthermore, the specific content of S2 includes:

[0021] S2.1: Calculate the basic contribution value of sub-nodes. The basic contribution value of a sub-node refers to the basic contribution degree jointly characterized by the connection relationship between the sub-node and other nodes in the entire hypernetwork and its own attributes. It is mainly measured by topological metric indicators and performance metric indicators. Topological metric indicators are parameter indicators used to describe and analyze the position and connectivity of nodes in a spatial form. Among these metric indicators, degree, super-degree, betweenness, and closeness are several commonly used key indicators. They have different meanings and functions and jointly constitute a comprehensive description of the characteristics of network nodes. Therefore, topological metric indicators use indicators such as degree, super-degree, betweenness, and closeness to describe the network position and connectivity of nodes. Performance metric indicators are key parameters for measuring the performance of network nodes and mainly characterize their own attributes and functions. Therefore, performance metric indicators consider three aspects. Among them, the reconnaissance subnet mainly considers performance indicators such as detection range and intelligence fusion ability, the command and control subnet mainly considers performance indicators such as command and decision-making ability and communication coverage, and the firepower subnet mainly considers performance indicators such as interception range and firepower coordinated combat ability. Figure 3 It is a schematic diagram of the scoring index system for the basic contribution value of nodes.

[0022] S2.2: Calculate the score of the subnet performance indicator. The score of the subnet performance indicator mainly measures the overall performance level of the subnet. However, when constructing the hypernetwork model of the air defense and antimissile kill network, it is also necessary to fully consider the influence of each subnet on the entire system. To quantify this influence, "super-degree" is used to describe the association degree between the subnet and other subnets. Multiply the node basic contribution value obtained in Section S2.1 by the corresponding super-degree value to obtain the node performance indicator score, and then sum the node performance indicator scores of all nodes under the same subnet to obtain the subnet performance indicator score. The calculation steps are as follows:

[0023] ① Calculate the node performance indicator score: Multiply each node basic contribution value NR i by the super-degree n H (i) to obtain the node performance indicator score:

[0024] PM i = n H (i) × NR i (1)

[0025] ② Calculate the subnet performance indicator score:

[0026]

[0027] S2.3: Calculate the overall efficiency of the hypernetwork. By summing up the performance index scores of the three subnets obtained in S2.2, the overall efficiency of the hypernetwork can be obtained. However, there are complex connections such as combat effectiveness, information sharing, and resource integration among the subnets. To ensure the efficient and stable operation as well as connectivity of the air defense and antimissile system, the "subnet correlation degree" is introduced to quantitatively reflect the tightness of the connection between subnets. This is crucial for understanding the structure and function of the entire air defense and antimissile kill network. The calculation steps are as follows:

[0028] ① Traversal of edges: Assume that the air defense and antimissile kill network hypernetwork has a total of r edges;

[0029] ② Labeling of edges: For each edge, if this edge connects subnet sn1 and sn2, then set If this edge does not connect these two subnets, then set it to 0;

[0030] ③ Calculation of correlation degree: Quantify the correlation degree among the reconnaissance subnet, command and control subnet, and firepower subnet It can be calculated through the following formula;

[0031]

[0032] ④ Performance index scores of subnets: The scores are obtained according to the functional importance in the reconnaissance subnet, command and control subnet, and firepower subnet networks, which are PM s , PM c and PM A ;

[0033] ⑤ Calculation of the overall efficiency of the hypernetwork.

[0034]

[0035] S2.4: On the basis of obtaining the overall efficiency of the hypernetwork, use the node deletion method to delete the nodes and connected edges to be evaluated, and recompute the overall efficiency by combining the calculation methods of S2.1, S2.2, and S2.3. The importance degree of the value of this node system is measured by comparing the ratio of the difference in the overall efficiency before and after node deletion to the overall efficiency before node deletion. The calculation formula is as follows:

[0036]

[0037] Among them, SV is the system value importance, CE is the overall efficiency of the original network, and CE' is the overall efficiency of the network after the node is deleted. The larger the value of SV, the greater the damage to the network function caused by the deleted node, which means the higher the value of the node. It should be noted here that when deleting the node to be evaluated, the edges of the node are also deleted, and the associated degree, superdegree, betweenness and closeness will change. The network morphology will not change except for the change at the deleted node.

[0038] Further, the step S2.1 mainly includes:

[0039] S2.1.1: Indicators such as detection distance, communication coverage and interception range can be obtained based on system simulation analysis or relevant reference materials.

[0040] S2.1.2: Intelligence fusion capability, command decision-making capability, firepower coordination capability, etc. are obtained through expert scoring based on the improved weighted method. The expert scoring method based on the improved weighted method is an optimization algorithm. For indicators that cannot be quantified, this method can be used to determine the final score. Through iterative and precise adjustments, it is ensured that the final score tends to the ideal value, ensuring the accuracy and objectivity of the results. It should be specially explained here that this method is to suppress irrational preferences by increasing the weight of experts to obtain more objective data.

[0041] S2.1.3: After obtaining the data of each indicator, input it into the entropy-grey correlation-TOPSIS method to obtain the weight of each indicator and the basic contribution value of each node. Combine the entropy weight method, grey correlation analysis method and TOPSIS method to give full play to their respective advantages, comprehensively consider the gap between the evaluation index and the optimal value and the closeness of the score to the ideal solution to make up for the lack of similarity in correlation, so as to obtain a more reliable score result. First, calculate the indicator weight by combining the entropy method, then objectively assign weights to each indicator, and finally calculate the basic contribution value of each node by weighted Euclidean metric and weighted grey correlation. Figure 4 This is the entropy-grey correlation-TOPSIS node algorithm flow chart.

[0042] Furthermore, the specific step S2.1.2 is implemented by the following method:

[0043] (1): Initialize the weights. All experts are equally important in the initial scoring.

[0044] (2): Usually based on their professional background, experience or other relevant criteria, each expert scores and calculates an initial weighted average:

[0045] (3) Combine the highest and lowest scores among the experts and the initial weighted average to calculate the first weight correction value:

[0046] (4) Combine the first weight correction value and the initial weight to calculate the corrected weight;

[0047] (5) Calculate the weighted average based on the first corrected weight;

[0048] (6) Perform iterative optimization, repeat the above steps, and execute the second iteration;

[0049] (7) Conduct convergence test, and end the iteration by continuously correcting the calculation until the preset convergence value is reached;

[0050] (8) Output the final scoring result;

[0051] Furthermore, in the expert scoring system, S experts participate in the scoring. In each iteration process, the weights and scoring values of each expert will be adjusted to optimize the final scoring result. The following is a detailed description of the relevant parameters:

[0052] ① represents the weight correction value of the i-th expert in the j-th iteration. This weight reflects the influence of the expert in the current iteration and is usually adjusted according to the closeness of the expert's score to the group consensus.

[0053] ② represents the weight calibration value of the i-th expert in the j-th iteration. This calibration value is used to adjust the initial weight of the expert, aiming to more accurately reflect the accuracy and reliability of their scoring.

[0054] ③ represents the weighted score of the i-th expert in the j-th iteration.

[0055] ④ represents the average of the weighted score values of all experts in the j-th iteration. This value provides a benchmark for the group consensus. Now define the correction value as follows:

[0056]

[0057] where represent the highest score and the lowest score given by the experts respectively.

[0058] This method is implemented through the following calculation steps:

[0059] ① Initialize the weights. All experts are equally important in the initial scoring. That is:

[0060] P i 0 = 1(7)

[0061] ② Usually based on their professional background, experience or other relevant criteria, each expert gives a score And calculate the initial weighted average:

[0062]

[0063] ③ Combine the highest score, the lowest score and the initial weighted average in the expert scoring, and calculate the first weight correction value as:

[0064]

[0065] ④ Combine the first weight correction value and the initial weight to calculate the corrected weight:

[0066]

[0067] ⑤ Combine the first corrected weight to calculate the weighted average:

[0068]

[0069] ⑥ Iterative optimization, repeat the above steps, perform the second iteration, and the second correction value is:

[0070]

[0071] ⑦ Convergence test, through continuous correction calculation, reach the preset convergence value, and the iteration ends, that is:

[0072]

[0073] In the formula: σ is the final number of iterations, and ε takes 0.01 in the present invention.

[0074] ⑧ Output the final scoring result, that is:

[0075]

[0076] Furthermore, the specific steps of the S2.1.3 are as follows:

[0077] Assume that there are n nodes and m indicators in the air defense and antimissile kill network, and the j-th indicator value in the i-th node is y ij , arrange them to form the node indicator value matrix Y, and use the maximum-minimum normalization method to generate the normalized matrix Y'. Its calculation formula is as follows:

[0078]

[0079] Each element in its matrix Y' is in [0,1], forming a new normalized matrix:

[0080]

[0081] The entropy value e j of the j-th indicator is recalculated as:

[0082]

[0083] Then the weight of the j-th index is:

[0084]

[0085] The index values after weight update can be obtained to form a new normalized matrix K:

[0086]

[0087] Determine the positive and negative ideal index values B + and B - , where and

[0088]

[0089] Then the weighted Euclidean metric values of each node to the positive and negative ideal systems are:

[0090]

[0091] In the formula is related to k ij and is related to, let is related to k ij and is related to, let

[0092] Calculate the grey correlation coefficient r ij , then the calculation formula of the grey correlation coefficient between the positive and negative ideal index systems of each node is:

[0093]

[0094] In the formula, ρ represents the resolution coefficient, and its general value range is (0 - 1). According to the present invention, ρ = 0.5 is taken. Thus, the coefficient matrix

[0095] Calculate the weighted grey correlation degree:

[0096]

[0097] Calculate the normalization processing of the weighted Euclidean distance and the weighted correlation degree:

[0098]

[0099] Calculate the comprehensive relative proximity value

[0100]

[0101] Where α + β = 1, α ∈ (0, 1), β ∈ (0, 1), and generally, α = β = 0.5 is often taken.

[0102] The relative closeness value, i.e., the node basic contribution value NR, is determined by combining the grey relational method and the TOPSIS method. i . The calculation formula is as follows:

[0103]

[0104] Compared with the prior art, the advantages of the present invention are as follows:

[0105] The present invention discloses a method for constructing a distributed air defense and anti-missile kill network and evaluating the value of nodes, constructs a distributed air defense and anti-missile kill network, accurately and effectively evaluates the importance of the value of key nodes, and has wide practicability in the related fields of air defense and anti-missile kill network system modeling and node value evaluation.

[0106] (1) A hyper-network model is constructed that reflects the complex network characteristics of heterogeneous nodes and multiple links in the distributed air defense and anti-missile kill network.

[0107] (2) At the same time, the improved weighted expert scoring is used to suppress the irrational preferences of experts in scoring, maintain the objective fairness of the scoring of data performance indicators, and bias towards maximizing the facts.

[0108] (3) The entropy weight method, grey relational analysis method, and TOPSIS method are combined to give full play to their respective advantages, comprehensively consider the importance of indicators, the degree of association between indicators, and the closeness of the scoring results to the ideal solution, so as to obtain more reliable results.

[0109] (4) The node deletion method is used to measure the importance of the value of nodes by comparing the degree of decline in the overall efficiency of the hyper-network before and after deleting nodes, and realize the quantitative analysis of the value of nodes in the air defense and anti-missile kill network. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 It is a schematic diagram for constructing the hyper-network model of the air defense and anti-missile kill network of the present invention;

[0111] Figure 2 It is a flow chart for evaluating the value of nodes of the present invention;

[0112] Figure 3 It is a schematic diagram of the scoring index system for the node basic contribution value of the present invention;

[0113] Figure 4 It is a flow chart of the entropy-grey relational-TOPSIS node algorithm of the present invention;

[0114] Figure 5Air defense and antimissile kill network visualization model for embodiments;

[0115] Figure 6 PM sorting of the basic contribution value of sub - nodes for embodiments;

[0116] Figure 7 SV sorting of the importance of the node system value for embodiments. Detailed implementation manners

[0117] The following describes the detailed implementation manners of the present invention in conjunction with embodiments:

[0118] It should be noted that the structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the implementation conditions of the present invention. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0119] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of clear narration, and are not used to limit the scope of implementation of the present invention. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope of implementation of the present invention.

[0120] Embodiment 1

[0121] The purpose of this embodiment is to provide a method for constructing a distributed air defense and antimissile kill network and evaluating node values, including the following steps:

[0122] S1: Construct an air defense and antimissile kill network super - network model composed of a reconnaissance sub - network, a command and control sub - network, and a firepower sub - network; Figure 1 It shows the schematic diagram of constructing the air defense and antimissile kill network super - network model.

[0123] S2: Establish a node value evaluation system; Figure 2 It is the flow chart of node value evaluation.

[0124] Further, the step S2 specifically includes:

[0125] S2.1: Calculate the basic contribution value of sub - nodes. Figure 3 It is the schematic diagram of the scoring index system for the basic contribution value of nodes.

[0126] Further, the step S2.1 mainly includes:

[0127] S2.1.1: Indexes such as detection range, communication coverage, and interception range can be obtained through system simulation analysis or relevant reference materials.

[0128] S2.1.2: The intelligence fusion ability, command and decision-making ability, firepower coordinated combat ability, etc. are obtained through expert scoring based on the improved weighted method.

[0129] S2.1.3: After obtaining the data of each index, input it into the entropy-grey correlation-TOPSIS method to obtain the weights of each index and the basic contribution values of each node. Figure 4 It is the flow chart of the entropy-grey correlation-TOPSIS node algorithm.

[0130] S2.2: Calculate the subnet performance index score, which mainly measures the overall performance level of the subnet.

[0131] S2.3: Calculate the overall effectiveness of the super network;

[0132] S2.4: On the basis of obtaining the overall effectiveness of the super network, use the node deletion method to delete the nodes and links to be evaluated, and recompute the overall effectiveness by combining the calculation methods of S2.1, S2.2, and S2.3. Measure the importance of the node system value by comparing the ratio of the difference in overall effectiveness before and after node deletion to the overall effectiveness before node deletion.

[0133] The specific implementation of this embodiment includes the following steps:

[0134] Step 1: To verify the effectiveness of the evaluation method proposed by the present invention, a simple regional air defense and anti-missile combat system is constructed for enemy targets (T1-T7), which includes 12 reconnaissance nodes (S1-S12), 10 command nodes (C13-C22), and 8 firepower nodes (A23-A30). Each node is numbered, and the nodes in the same subnet are set with the same color (green represents reconnaissance sub-nodes, blue represents command sub-nodes, and red represents firepower sub-nodes). As Figure 5 shown.

[0135] Step 2: Node index value calculation. Since the performance indicators of different subnets are different, when calculating the subnet performance index score, strict initialization processing of the performance indicators is required to maintain the comparability of data indicators. A numerical benchmark (10 -8 ) is set for the performance indicators that have no association with the subnet. During the evaluation process, non-quantifiable indicators with strong subjectivity need to be emphasized, and the modified weighted method of expert scoring is adopted. The specific approach is to invite 5 experts to score and evaluate this indicator, and the scoring system strictly follows the ten-point scale standard.

[0136] Taking the A23 node in the firepower subnet as an example, when evaluating its firepower coordinated combat ability, the expert scoring method is strictly followed. Among them, the parameters u, and p respectively represent the correction value of the weight and the modified weight, X 0This is the initial scoring by experts, and the results are shown in Table 1.

[0137] Table 1 Revised Weighted Deduction Table of Expert Scoring for Firepower Node A23 (Firepower Coordination Combat Capability)

[0138]

[0139]

[0140] As can be seen from Table 1, at the 13th iteration, the set conditions are met, so the firepower coordination combat capability score of node A23 can be obtained as For non - quantifiable performance metric indicators such as the intelligence fusion ability, command decision - making ability, and firepower coordination combat ability of other nodes, the corresponding index scores are also calculated by this method respectively.

[0141] For the numerical calculations of detection range, communication coverage, and interception range in the performance metric indicators, they are obtained by querying relevant reference materials, combat standards, etc. For the detection range, the average value and the minimum value of the parameters are taken, and the minimum value of the parameters is taken for both the communication coverage and the interception range.

[0142] Secondly, according to the node interaction relationship and relevant calculation formulas, the topological metric indicators such as node degree, betweenness, and closeness are calculated using Python programming software to obtain Table 2.

[0143] Table 2 Python Solution of Node Degree, Betweenness, and Closeness

[0144]

[0145] To sum up, the index value table of each node of the hyper - network is shown in Table 3.

[0146] Table 3 Hyper - network Node Index Values

[0147]

[0148]

[0149] Step 3: Calculate the overall efficiency E of the hyper - network according to the index values of all nodes in Table 3.

[0150] First, use the entropy - grey correlation - TOPSIS method to calculate the index weights: w j = [0.21, 0.06, 0.17, 0.08, 0.18, 0.13, 0.05, 0.03, 0.09]. Form the normalized matrix Y' according to the maximum - minimum normalization method for the index values in Table 3, as shown in Table 4 below.

[0151] Table 4 Normalized Matrix Y'

[0152]

[0153] Step 4: According to the grey relational calculation formula, the weighted Euclidean metric value and grey relational degree are obtained, as shown in Table 5.

[0154] Table 5 Weighted Euclidean metric and grey relational degree

[0155]

[0156]

[0157] Step 5: Combined with the TOPSIS model algorithm, let α = β = 0.5, and calculate the data in Table 5 to obtain the node basic contribution value NR i , the results are shown in Table 6.

[0158] Table 6 Subnode basic contribution value

[0159]

[0160] Step 6: Calculate the node over-degree, node performance index score, correlation, subnet performance index score and overall performance of each node, as shown in Table 7.

[0161] Table 7 Overall performance of the hypernetwork

[0162]

[0163]

[0164] Step 7: Combined with Table 7, we can get the performance index score PM of the reconnaissance subnet S =11.47, the performance index score PM of the control subnet C =23.61, the performance index score PM of the firepower subnet A =9.59, the overall efficiency CE of the super network is 640.92. Combining the basic contribution values ​​PM of each sub-node in Table 7, the ranking is as follows Figure 6

[0165] Step 8: Use the node deletion method to delete nodes S1-A30 in turn, repeat the above calculation process, and calculate the overall performance after deleting each node. Then, the system value importance SV of each node can be obtained. The results of sorting the system value importance SV are as follows: Figure 7As shown in the figure. The value of the air defense and anti-missile kill network system is closely related to the functional attributes of the nodes, the location distribution, and the number of connected nodes. In the overall structure of the hypernetwork, nodes such as C14, C17, and C19 show high superdegree, degree, and betweenness characteristics, and have high performance metric index values. Once their nodes are removed, they will have a significant impact on the entire network, which reflects that C14, C17, and C19 have a high value status in the network system. In the reconnaissance subnet, S12 has the largest degree and superdegree. Its deletion will cause a significant decrease in the overall efficiency of the supernet. Therefore, it is at a high value level in the basic contribution value of the subnode and the system value. In the command subnet, the command nodes (such as C14, C20, and C17) perform well in both basic scores and system values, significantly higher than the reconnaissance nodes and firepower nodes. As for the firepower nodes of the firepower subnet, through analysis and comparison, both the basic contribution value of the nodes and the importance of the node system value are basically at a relatively low level in the entire network system, among which A23, A24, A25, and A26 are in the bottom 5 positions in the entire supernet. By comprehensively comparing the basic contribution value of the nodes and the importance of the system value, the importance of both can be expressed as: command subnet (node) > reconnaissance subnet (node) > firepower subnet (node).

[0166] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the field without departing from the purpose of the present invention. Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A distributed air defense and anti-missile killing network construction and node value assessment method, characterized in that: The following steps are involved: S1: Construct an air defense and anti-missile kill network super network model consisting of a reconnaissance subnet, a command and control subnet, and a firepower subnet; Construct the air defense and anti-missile kill network super network model H, which is divided into reconnaissance subnet S, command subnet C and firepower subnet A according to different duties and functions, then H = S ∪ C ∪ A; each subnet consists of a node set V = {v1, v2, …, v n } and edge set E = {e1,e2,…,e n }, the node set is divided into reconnaissance nodes V S 、Command node V C and Firepower Node V A , that is: V = (V S ,V C ,V A ); The edge set E is divided into E S 、E C and E A , while the interactions between different types of nodes build connections between subnetworks; that is: E H =E S ∪E C ∪E A ∪E SC ∪E CA ∪E AS ; S2: Establish a node value assessment system; The node value assessment method consists of four steps; The first step is to calculate the basic contribution value NR of each sub-node by combining the node topology metric and performance indicator relationship. i ; The second step is to calculate the basic contribution value NR of all sub-nodes in the same sub-network. i The sum is the performance index score PM of each subnet i ; The third step is to calculate the performance index scores of the reconnaissance subnet, command subnet and firepower subnet PM i The sum is obtained to obtain the overall efficiency CE of the super network; The fourth step is to calculate the overall performance difference after deleting the node to be evaluated through the node deletion method, and evaluate the value importance SV of the node system.

2. The method for constructing a distributed air defense and anti-missile killing network and evaluating node value according to claim 1 is characterized in that: The step S1 specifically includes: S1.1: Construct a reconnaissance subnet model. The reconnaissance subnet is described as (V S ,E S );Responsible for collecting, processing and analyzing battlefield information;Reconnaissance node V S Indicates the nodes including radar ground, satellite receiving station, early warning aircraft, and sonar detection equipment. S It represents the information transmission path and cooperation relationship between different reconnaissance nodes, and the actual communication link and information sharing path between nodes; S1.2: Construct the command subnet, which is described as (V C ,E C ), reflects the efficiency of battlefield command, control and coordination, which can be characterized by command decision-making ability, that is, the ability of command nodes to dispatch and issue instructions to different combat units and resources; command node V C It includes command centers at all levels, communication networks, and data processing center nodes, responsible for formulating combat plans, making decisions, and commanding troop actions; C It represents the communication links and command relationships between command and control nodes, carries the transmission of command orders, battlefield situation information and decision support data, and reflects the hierarchical and collaborative nature of the command structure; S1.3: Build a firepower subnet, which is described as (V A ,E A ), reflects the firepower strike effectiveness and response speed on the battlefield, and is described by the firepower strike capability, that is, the firepower node's strike accuracy and coverage of different targets; the firepower node V A Indicates ground-based air defense and anti-missile weapon systems and laser weapon interception equipment nodes; E A It represents the key functions of information sharing, coordinated strike, decision feedback and fire support among fire units; S1.4: Construction of the relationship between subnets. The reconnaissance subnet is a platform for battlefield information transmission and processing, and it is for different nodes of the command and control subnet to share and exchange intelligence data. The relationship between the reconnaissance subnet and the command and control subnet is mathematically described as E SC .

3. The method for constructing a distributed air defense and anti-missile killing network and evaluating node value according to claim 2 is characterized in that: The command subnet receives intelligence information from the reconnaissance subnet and is responsible for delivering decision instructions to the firepower subnet. The relationship between the reconnaissance subnet, the command subnet, and the firepower subnet is established through the edge set E SC and E CA Give a description; The firepower subnet receives the instructions and decisions of the command and control subnet and executes the firepower strike mission; the command node in the command and control subnet provides the firepower subnet with target information and strike timing, and the firepower subnet feeds back the strike effect and battlefield changes to the command and control subnet; the mutual relationship between the command and control subnet and the firepower subnet is realized through E CA Give a description.

4. The method for constructing a distributed air defense and anti-missile killing network and evaluating node value according to claim 1 is characterized in that: The S2 specifically includes: S2.1: Calculate the basic contribution value of the subnode. The basic contribution value of the subnode refers to the basic contribution degree of the subnode in the entire super network, which is jointly characterized by the connection relationship between the subnode and other nodes and its own attributes. It is measured by topological metrics and performance metrics. Among them, the topological metrics, degree, superdegree, betweenness, and closeness, characterize the network position and connectivity of the node. The performance metrics consider three dimensions, among which the reconnaissance subnet considers the detection distance and intelligence fusion capability dimension indicators, the command and control subnet considers the command decision-making capability and communication coverage dimension indicators, and the firepower subnet considers the interception range and firepower coordinated combat capability dimension indicators. S2.2: Calculate the subnet performance index score, which measures the overall performance level of the subnet. When constructing the super network model of the air defense and anti-missile kill network, consider the influence of each subnet on the entire system. In order to quantify this influence, use "super-degree" to describe the degree of association between a subnet and other subnets. Multiply the basic contribution value of the node obtained in S2.1 above by the corresponding super-degree value to obtain the node performance index score, and then sum up the performance index scores of all nodes in the same subnet to obtain the subnet performance index score. The calculation steps are as follows: ① Calculate the node performance index score: The basic contribution value NR of each node i With transcendence H (i) Multiply to get the performance index score of the node: PM i =n H (i)×NR i (1) ② Calculate the subnet performance index score: S2.3: Calculate the overall effectiveness of the super network, add the performance index scores of the S2.2 subnets, and sum the scores of the three subnets to get the overall effectiveness of the super network; there are complex connections between the combat effectiveness, information sharing and resource integration between the subnets. In order to ensure the efficient and stable operation and connectivity of the air defense and anti-missile system, the "subnet association" is introduced to quantify the closeness of the subnet connection; the calculation steps are as follows: ① Edge traversal: Assume that the air defense and anti-missile killing network super network has a total of r edges; ② Edge marking: For each edge, if this edge connects subnets sn1 and sn2, set If this line does not connect the two subnets, set it to 0; ③ Calculation of correlation: Quantify the correlation between the reconnaissance subnet, the command subnet and the firepower subnet Calculated by the following formula; ④ Subnet performance index score: The score is calculated based on the importance of the functions in the reconnaissance subnet, command subnet, and firepower subnet, which are PM s , PM c and PM A ; ⑤ Calculation of the overall performance of the hypernetwork; S2.4: On the basis of obtaining the overall performance of the hypernetwork, the node deletion method is used to delete the nodes and edges to be evaluated, and the overall performance is re-calculated by combining the calculation methods of S2.1, S2.2, and S2.

3. The value importance of the node system is measured by comparing the ratio of the overall performance difference before and after the node deletion to the overall performance before the node deletion; the calculation formula is as follows: Among them, SV is the system value importance, CE is the overall efficiency of the original network, and CE' is the overall efficiency of the network after the node is deleted; the larger the value of SV, the greater the damage to the network function caused by the deleted node, which means the higher the value of the node.

5. The method for constructing a distributed air defense and anti-missile killing network and evaluating node value according to claim 4 is characterized in that: The step S2.1 comprises: S2.1.1: The detection range, communication coverage and interception range indicators are obtained based on system simulation analysis or relevant reference materials; S2.1.2: Intelligence fusion capability, command decision-making capability, and firepower coordination capability are obtained through expert scoring based on the improved weighted method; S2.1.3: After obtaining the data of each indicator, input it into the entropy-grey correlation-TOPSIS method to obtain the weight of each indicator and the basic contribution value of each node; first, calculate the indicator weight by combining the entropy method, then objectively assign weights to each indicator, and finally calculate the basic contribution value of each node through weighted Euclidean measurement value and weighted grey correlation degree.

6. The method for constructing a distributed air defense and anti-missile killing network and evaluating node value according to claim 5 is characterized in that: In the expert scoring system, S experts participate in the scoring. In each iteration, the weight and score of each expert will be adjusted to optimize the final scoring result. The following is a detailed description of the relevant parameters: ①P i j : represents the weight correction value of the i-th expert at the jth iteration; This weight reflects the influence of the expert in the current iteration and is adjusted according to how close the expert's rating is to the group consensus; ② represents the weight correction value of the i-th expert at the jth iteration; this correction value is used to adjust the initial weight of the expert; ③ represents the weighted score of the i-th expert at the j-th iteration; ④ It represents the average of all the experts’ weighted scores in the jth iteration; this value provides a benchmark for group consensus; the correction value is now defined as follows: In the formula Respectively represent the highest and lowest scores given by experts; S2.1.2 The specific steps are achieved by the following method: ① Initialize the weights. All experts are equally important in the initial scoring; that is: P i 0 ＝1 (7) ② Each expert usually scores based on their professional background, experience or other relevant criteria And calculate the initial weighted average: ③ Combine the highest and lowest scores in the expert scoring and the initial weighted average to calculate the first weight correction value: ④ Combine the first weight correction value and the initial weight to calculate the corrected weight: ⑤ Combine the weights after the first revision and calculate the weighted average: ⑥ Iterative optimization, repeat the above steps, perform the second iteration, the second correction value is: ⑦ Convergence test: by continuously correcting the calculation, the preset convergence value is reached and the iteration ends, that is: Where: σ is the last iteration number, ε is 0.01; ⑧Output the final scoring result, namely:

7. The method for constructing a distributed air defense and anti-missile killing network and evaluating node value according to claim 5 is characterized in that: The S2.1.3 step is as follows: Suppose there are n nodes and m indicators in the air defense and anti-missile killing network, and the value of the jth indicator in the i-th node is y ij , arrange them to form the node index value matrix Y, and use the maximum and minimum normalization method to generate the normalized matrix Y'; its calculation formula is as follows: Each element in its matrix Y' is in [0,1], forming a new normalized matrix: The entropy value e of the j-item indicator j The recalculation of is: Then the weight of the j-item indicator is: Get the index value after weight update to form a new normalized matrix K: From the obtained normalized matrix, determine the positive and negative ideal index values ​​B + and B - ,in and Then the weighted Euclidean metric of each node to the positive and negative ideal system is: In the formula With k ij and related, With k ij and About, order Calculate the grey correlation coefficient r ij , then the grey correlation coefficient calculation formula between the positive and negative ideal index systems of each node is: Where ρ represents the resolution coefficient, and its value range is (0-1); This gives the coefficient matrix Calculate the weighted grey relational degree: Calculate the normalized weighted Euclidean distance and weighted association: Calculate the comprehensive relative proximity value and Among them, α+β=1, α∈(0,1), β∈(0,1), and the value α=β=0.5; Combine the grey correlation method and TOPSIS method to determine the relative close value, that is, the node basic contribution value NR i ; The calculation formula is as follows:

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