Border road network multi-target attack sequencing method based on sub-network function relationship

Abstract

The application discloses a kind of border road network multi-objective attack sequencing methods based on sub-network function relationship, first the node of different functional attributes is classified as sub-network, and the value evaluation index system of node in each sub-network is constructed;With the weighting method of the fusion of entropy weight TOPSIS method and modified weighting method, the weight of each sub-network index and the score of each node are calculated;The relationship between sub-networks is calculated, and then the overall function score of the border military exchange network is obtained;Finally, the impact degree on network function score after deleting node generates attack sequencing.The application fully considers the actual operational requirements of border special battlefield environment, strengthens the research on the value of road network location and transportation level of target, relies on the function relationship between sub-networks, such as war readiness transfer transportation, accurately and reasonably discusses the value of target, and obtains the importance degree sequencing.

Description

Technical Field

[0001] This invention belongs to road network management technology, specifically a method for ranking multi-target attacks on border road networks based on sub-network functional relationships. Background Technology

[0002] Existing analyses of the value of target nodes in military road networks mainly fall into two categories. The first directly applies traditional node importance assessment methods to the study of military target nodes. This involves analyzing military characteristics to propose a targeted indicator system suitable for the battlefield environment and improving the importance assessment algorithm to align with that environment. The second approach uses military operational tactics as the analytical framework, incorporating node value indicators into various tactics for analysis, and comprehensively evaluating them to arrive at strike decisions.

[0003] Compared to the first type of scheme, which focuses solely on refined indicators and improved algorithms, the second type of strike decision generation scheme takes into account more of the actual characteristics of the battlefield environment and the tactical situations during fire strikes. It can better adapt to the complex and ever-changing battlefield environment and has more room for in-depth research. However, the current analytical framework of this type of research method generally focuses on information flow and firepower allocation, ignoring the topological location value of target nodes at the transportation level. In the large-scale battlefield environment of the border, there are fewer physical building targets, information flow is simple, and the deployment and mobilization of troops and materials heavily rely on the transportation of the road network. Under these circumstances, existing multi-target importance ranking research schemes are obviously no longer able to meet the actual needs of the border road network environment. Summary of the Invention

[0004] To address the aforementioned technical deficiencies in the existing technology, this invention proposes a multi-target attack ranking method for border road networks based on sub-network functional relationships.

[0005] The technical solution to achieve the purpose of this invention is: a method for ranking multi-target attacks on border road networks based on sub-network functional relationships, the specific steps of which are as follows:

[0006] The targets in the road network are divided into three sub-networks based on their battlefield functions;

[0007] Establish an indicator system and determine objective indicators, including common indicators for network topology and characteristic auxiliary indicators for each sub-network.

[0008] Calculate the common indicators of network topology and the characteristic auxiliary indicators of each sub-network to obtain objective indicator data;

[0009] The subjective indicator data is obtained by processing the expert scores using a modified weighting method.

[0010] Based on subjective and objective indicator data, node scores are obtained using the entropy-weighted TOPSIS method.

[0011] The subnetwork function score is calculated based on the relative proximity of the i-th evaluation object to the maximum value.

[0012] Calculate the degree of correlation between subnetworks;

[0013] Calculate the overall functional score of the border military-civilian communication network;

[0014] Calculate the decrease in network function score after deleting nodes, obtain the node value ranking, and generate a multi-target attack strategy for the border road network based on the importance of node value.

[0015] Preferably, the specific method for dividing targets in the road network into three sub-networks according to battlefield function is as follows:

[0016] Road network intersections, bridges, and tunnel nodes are designated as traffic hub subnets; barracks and artillery positions are designated as military force subnets; and warehouses and transportation stations are designated as logistics support subnets.

[0017] Preferably, the network topology dimension indicators include the degree, betweenness, and density of nodes; the characteristic auxiliary indicators of each sub-network include the capacity and load of traffic units in the transit hub sub-network, the firepower level and defense capability in the military force sub-network, and the material reserve and material transportation volume in the logistics support sub-network.

[0018] Preferably, the data for the characteristic auxiliary indicators of each sub-network are the collected data on traffic unit capacity, traffic unit load, material reserves, and material transportation volume;

[0019] The topology dimension metrics are as follows:

[0020] Degree of a node:

[0021] C A (i)=n

[0022] In the formula, n is the number of neighboring nodes of a node;

[0023] Betweenness:

[0024]

[0025] In the formula, g jk G represents the number of shortest paths between node j and node k. jk This represents the number of shortest paths between node j and node k that pass through node i.

[0026] Tightness:

[0027]

[0028] In the formula, d ijThis represents the distance between node i and node j.

[0029] Preferably, the specific method for processing expert scores on subjective indicators using the modified weighting method to obtain subjective indicator data is as follows:

[0030] set up Let be the corrected weight of the i-th expert in the j-th iteration. This represents the adjusted weight value for the i-th expert in the j-th iteration. Let be the weighted score value of the i-th expert in the j-th iteration. Let m be the weighted average value of the j-th iteration, and m be the number of expert raters. The initial weights are assigned as j = 0.

[0031] Calculate the initial weighted average

[0032]

[0033] First correction value

[0034]

[0035] Weights after the first adjustment

[0036]

[0037] Calculate the weighted average of the first iteration.

[0038]

[0039] Calculate the second correction value

[0040]

[0041] Continuously adjust the weights and iterate until...

[0042]

[0043] The final score is

[0044]

[0045] Preferably, the specific method for obtaining node scores using the entropy-weighted TOPSIS method is as follows:

[0046] Let n be the number of target nodes in the road network, m be the number of indicators, and t be the score of the j-th indicator for the i-th target. ij Arrange them to form the target node matrix T;

[0047] Generate normalized matrix T'

[0048]

[0049] Calculate the entropy value e j

[0050]

[0051] Calculate the entropy weight w j

[0052]

[0053] Calculate the entropy weight attribute value

[0054]

[0055] Positive and negative ideal points in vector space

[0056]

[0057]

[0058] Calculate the distance between each target and the positive and negative ideal points.

[0059]

[0060]

[0061] Calculate the relative proximity S between the i-th evaluation object and the maximum value. i

[0062]

[0063] Preferably, the sub-network function score is calculated based on the relative proximity of the i-th evaluation object to the maximum value. The specific method is as follows:

[0064] Let the superdegree of the i-th node be k. i The functional score of node i is Q. i

[0065]

[0066] The functional score of the k-th subnetwork is:

[0067]

[0068] Preferably, the overall functional score of the border military communication network is calculated, assuming that after traversal, there are r' edges connecting the two subnets, with a length of U. n ,use To reflect the close relationship between the two sub-networks within the overall military transportation network:

[0069]

[0070] Let the overall functional score of the border military-civilian communication network be V, specifically:

[0071] V = Q 1 (K 12 G 12 +K 13 G 13 )+Q 2 (K 21 G 21 +K 23 G 23 )+Q 3 (K 31 G 31 +K 32 G 32

[0072] In the formula, Q i G scores the functionality of subnet i. ij K represents the correlation between subnet i and subnet j. ij The degree of closeness between subnet i and subnet j.

[0073] Preferably, the decrease in network function score after deleting a node is calculated to obtain the node value ranking, and a multi-target attack strategy for the border road network based on the importance of node value is generated. The specific process is as follows:

[0074] Suppose that after deleting node i, the overall network performance score after recalculation is V'. i The degree of decline in network function score O i for

[0075]

[0076] O i The larger the value of node i, the higher the value of node i, according to O i The data is sorted to generate a multi-target strike strategy for the border road network based on the importance of node value.

[0077] Compared with the prior art, the significant advantages of this invention are: this invention fully considers the actual combat needs of the special battlefield environment on the border, strengthens the research on the value of the target's road network location and transportation, and relies on the functional relationships of combat readiness transfer and transportation between sub-networks to discuss the value of the target more accurately and reasonably, and obtain the importance ranking.

[0078] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0079] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0080] Figure 1 This is a flowchart of the present invention.

[0081] Figure 2 This is a schematic diagram of the network structure constructed according to an embodiment of the present invention. Detailed Implementation

[0082] It is readily understood that, based on the technical solution of this invention, various embodiments of the invention can be conceived by those skilled in the art without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention. Rather, these embodiments are provided to enable those skilled in the art to gain a more thorough understanding of the invention. Preferred embodiments of the invention are described below in conjunction with the accompanying drawings, which form part of this application and, together with the embodiments of the invention, serve to illustrate the innovative concept of the invention.

[0083] The present invention is conceived as a method for ranking multi-target attacks on border road networks based on sub-network functional relationships, comprising:

[0084] S1: Utilizing the concept of hypernetworks, the targets in the road network are divided into three subnetworks based on battlefield function, specifically:

[0085] Road network intersections, bridges, tunnels and other nodes are set as traffic hub subnetworks; barracks, artillery positions and other sites are set as military force subnetworks; warehouses, transportation stations and other sites are set as logistics support subnetworks.

[0086] S2: Establish an indicator system and determine the common indicators for network topology and the characteristic auxiliary indicators for each sub-network.

[0087] The metrics for evaluating nodes in each subnetwork include common network topology dimensions, such as node degree, betweenness, and density; as well as characteristic ancillary metrics for each network, such as traffic unit capacity and traffic unit load in the transit hub subnetwork, firepower level and defense capability in the military force subnetwork, and material reserves and material transportation volume in the logistics support subnetwork.

[0088] S3: Collect information and calculate to obtain objective indicator data.

[0089] Collect data on transportation unit capacity, transportation unit load, material reserves, and material transportation volume.

[0090] Calculate topology dimension metrics:

[0091] (1) Degree of a node

[0092] C A (i)=n

[0093] In the formula, n is the number of neighboring nodes of a node.

[0094] ⑵ Betweenness

[0095]

[0096] In the formula, g jk G represents the number of shortest paths between node j and node k. jk This represents the number of shortest paths between node j and node k that pass through node i.

[0097] (3) Tightness

[0098]

[0099] In the formula, d ij This represents the distance between node i and node j.

[0100] S4: Use the modified weighting method to process the experts' scores on the subjective indicators and obtain the data of the subjective indicators.

[0101] In order to obtain more objective data for subjective indicators such as firepower level and defense capability in the indicator evaluation system, this invention chooses to use expert scoring results as the basis, adjust the expert weights to suppress the irrational bias of the scorers, and obtain the final score value.

[0102] set up Let be the corrected weight of the i-th expert in the j-th iteration. This represents the adjusted weight value for the i-th expert in the j-th iteration. Let be the weighted score value of the i-th expert in the j-th iteration. Let m be the weighted average value of the j-th iteration, and m be the number of expert raters.

[0103] Assign initial weights as (j=0)

[0104]

[0105] Calculate the initial weighted average

[0106]

[0107] First correction value

[0108]

[0109] Weights after the first adjustment

[0110]

[0111] Calculate the weighted average of the first iteration.

[0112]

[0113] Calculate the second correction value

[0114]

[0115] Continuously adjust the weights and iterate until...

[0116]

[0117] The final score is

[0118]

[0119] S5: Calculate the scores of all nodes using the entropy weight TOPSIS method. Input the final scores of subjective indicators and the data values ​​of objective indicators into the analysis process of the entropy weight TOPSIS method to obtain the node scores.

[0120] Let the number of target nodes in the road network be n, and the j-th data value or final score value of the i-th target be t. ij Arrange them to form the target node matrix T.

[0121] Generate normalized matrix T'

[0122]

[0123] Calculate the entropy value e j

[0124]

[0125] Calculate the entropy weight w j

[0126]

[0127] Calculate the entropy weight attribute value

[0128]

[0129] Positive and negative ideal points in vector space

[0130]

[0131]

[0132] Calculate the distance between each target and the positive and negative ideal points.

[0133]

[0134]

[0135] Calculate the relative proximity S between the i-th evaluation object and the maximum value. i

[0136]

[0137] S6: Calculate the sub-network function score based on the relative proximity of the i-th evaluation object to the maximum value. The specific method is as follows:

[0138] Let the superdegree of the i-th node be k. i The functional score of node i is Q. i

[0139]

[0140] The functional score of the k-th subnetwork is:

[0141]

[0142] S7: Calculate the correlation between sub-networks, the specific method is as follows:

[0143] The degree of association between subnets is the sum of the number of edges connecting nodes in the two subnets. Iterating through all r edges, the connection between two subnets is denoted as b. z =1, otherwise record as b z =0, then the correlation between the k1th subnet and the k2th subnet is... for

[0144]

[0145] S8: Calculate the overall functional score of the border military communication network. Assume that after traversal, there are r' edges connecting two subnets, with a length of U. n ,use To reflect the close relationship between the two sub-networks within the overall military transportation network:

[0146]

[0147] Let V be the overall functional rating of the border military-civilian communication network;

[0148] V = Q 1 (K 12 G 12 +K 13 G 13 )+Q 2 (K21 G 21 +K 23 G 23 )+Q 3 (K 31 G 31 +K 32 G 32 )

[0149] In the formula, Q is the subnet functional score calculated in S6, G is the subnet correlation degree calculated in S7, and K is the degree of tightness.

[0150] S9: Calculate the decrease in network function score after deleting nodes, obtain the node value ranking, and generate a multi-target attack strategy for the border road network based on the importance of node value. The specific process is as follows:

[0151] Suppose that after deleting node i, the overall network performance score after recalculation is V'. i The degree of decline in network function score O i for

[0152]

[0153] Q i The larger the value of node i, the higher the value of node i, according to O i The data is sorted to generate a multi-target strike strategy for the border road network based on the importance of node value.

[0154] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0155] It should be understood that, in order to simplify the present invention and help those skilled in the art understand its various aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes described in a single embodiment or with reference to a single figure. However, the present invention should not be construed as including all features in the exemplary embodiments as essential technical features of the claims of this patent.

[0156] It should be understood that the modules, units, components, etc., included in the device of one embodiment of the present invention can be adaptively changed to be placed in a device different from that embodiment. Different modules, units, or components included in the device of the embodiment can be combined into a single module, unit, or component, or they can be divided into multiple sub-modules, sub-units, or sub-components.

[0157] Example

[0158] To verify the rationality of this invention, the network constructed in this embodiment is as follows: Figure 2 .

[0159] Among them, circular nodes represent nodes of the military force subnet, square nodes represent nodes of the transit hub subnet, and triangular nodes represent nodes of the logistics support subnet.

[0160] (1) Indicator Data Processing

[0161] Each subnetwork has different characteristics. The values ​​of the characteristic indicators for each subnetwork are calculated accordingly. For characteristic indicators that do not belong to that subnetwork, a very small value is assigned to them (10 in this embodiment). -6 )

[0162] Table 1. Indicator Data Processing

[0163]

[0164] (2) Node Function Scoring

[0165] The functional score of each node was obtained using the entropy weight method, and the results are shown in the table below:

[0166] Table 2 Node Function Scoring

[0167]

[0168] (3) Network Functional Score

[0169] The score Q of all nodes belonging to the same subnetwork i By summing the scores, we obtain the functional scores of the three sub-networks:

[0170] Q_blue = 3.851838 Q_red = 6.432621 Q_yellow = 8.105866

[0171] The following results were obtained by calculating the subnetwork correlation degree:

[0172] G_red_blue = 10 G (Red / Yellow) = 5 G yellow blue = 17

[0173] Calculate the tightness K i :

[0174] K_red = 0.02678 Kred = 0.01326 K = 0.0228

[0175] The overall network performance score is V:

[0176] V = Q 1 (K 12 G 12 +K 13 G 13 )+Q 2 (K 21 G 21 +K 23 G23 )+Q 3 (K 31 G 31 +K 32 G 32 ) = 7.32002

[0177] (4) Calculating node value using the node deletion method

[0178] Delete each node in turn and calculate the degree of descent O. i This yields the system value and attack ranking for each node:

[0179]

[0180]

Claims

1.A method for border road network multi-target attack sequencing based on sub-network function relationship, characterized in that, The specific steps are as follows: The target in the road network is divided into three sub-networks according to battlefield functions, and the specific method is as follows: The intersection, bridge and tunnel nodes in the road network are set as the traffic hub sub-network; the barracks and artillery position nodes are set as the military force sub-network; and the warehouse and transportation station nodes are set as the logistics support sub-network; An index system is built, and objective indexes are determined, wherein the objective indexes are composed of network topology dimension public indexes and sub-network characteristic auxiliary indexes; The network topology dimension public indexes are composed of the degree, betweenness and closeness of the nodes; and the sub-network characteristic auxiliary indexes are composed of the traffic unit capacity and traffic unit load in the traffic hub sub-network, the material reserve amount in the logistics support sub-network, and the material transportation amount; The network topology dimension public indexes and the sub-network characteristic auxiliary indexes are calculated to obtain objective index data; The topology dimension public index data are as follows: The degree of the node is as follows: C A (i) = n In the formula, n is the number of neighbor nodes of the node. The betweenness is as follows: where g jk denotes the number of shortest paths between node j and node k, g jk (i) denotes the number of shortest paths between node j and node k via node i; The closeness is as follows: where d ij denotes the distance between node i and node j; The subjective index data are obtained by using the modified weighting method to process the scoring of the experts on the subjective indexes, and the subjective data include the fire power and defense capability in the military force sub-network, and the specific method is as follows: Let Wij is the modified weight value of the ith expert in the jth iteration, Wij is the modified weight value of the ith expert in the jth iteration, Wij is the weighted score value of the ith expert in the jth iteration, Wij is the weighted average value of the jth iteration, m is the number of expert raters, and the initial weight is j = 0, The initial weighted average value is calculated The first correction value is calculated The weight value after the first correction is calculated The weighted average value of the first iteration is calculated The second correction value is calculated The weight value is continuously corrected, and iteration is performed until The final score value is as follows: The node score is obtained by using the entropy weight TOPSIS method according to the subjective index data and the objective index data, and the specific method is as follows: Let the number of target nodes in the road network be n, the number of indexes be m, and the score of the jth index of the ith target be t ij , and arrange them into a target node matrix T; The normalized matrix T' is generated calculating the entropy value e j Computing entropy weight w j Computing entropy-weighted attribute values The positive and negative ideal points in the vector space The distance between each target and the positive and negative ideal points is calculated S = (i - max) / (max - min) i The sub-network function score is calculated according to the relative closeness of the distance between the ith evaluation object and the maximum value, and the specific method is as follows: Let the hyperdegree of the ith node be k i , and the function score of node i be Q i Q i = k i S i The function score of the kth sub-network is as follows The correlation degree between the sub-networks is calculated, and the specific method is as follows: The correlation degree between the sub-networks is the sum of the number of edges connected between the nodes of the two sub-networks, and all r edges are traversed, and the connection between the two sub-networks is recorded as b z = 1, otherwise b z = 0, then the correlation degree between the k1th sub-network and the k2th sub-network is ​ Calculate the overall functional score of the border military communication network, assuming that after traversal, there are r edges connecting two subnets. ' There are 1, with a length of U. n ,use To reflect the close relationship between the two sub-networks within the overall military transportation network: The overall function score of the border military exchange network is V, and the specific method is as follows: V = Q 1 (K 12 G 12 + K 13 G 13 ) + Q 2 (K 21 G 21 + K 23 G 23 ) + Q 3 (K 31 G 31 + K 32 G 32 ) In the formula, is a function score of the subnet k1, is an association degree of the subnet k1 and the subnet k2, is a closeness degree of the subnet k1 and the subnet k2; The descending value of the network function score after the node is deleted is calculated to obtain the node value order, and the border road network multi-objective attack strategy based on the node value importance is generated, and the specific process is as follows: Let the node i is deleted, the network overall function score of re-calculation is V' i , the degree of decline of network function score O i is O i The greater, the value of the node i, according to the data sorting, the border road network multi-objective strike strategy based on the node value importance is generated. i The greater, the value of the node i, according to the data sorting, the border road network multi-objective strike strategy based on the node value importance is generated.

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