CN115022192B - Resource selection method and system for evolutionary game network information system - Google Patents
Resource selection method and system for evolutionary game network information system Download PDFInfo
- Publication number
- CN115022192B CN115022192B CN202210615542.4A CN202210615542A CN115022192B CN 115022192 B CN115022192 B CN 115022192B CN 202210615542 A CN202210615542 A CN 202210615542A CN 115022192 B CN115022192 B CN 115022192B
- Authority
- CN
- China
- Prior art keywords
- node
- decision
- expressed
- task
- resource
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Algebra (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Pure & Applied Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a system for selecting resources of an evolution game network information system in the field of network resource allocation, wherein the method comprises the following steps: acquiring a decision space in a military countermeasure network, and constructing a task completion degree function in the task execution process of each resource node in the decision space; superposing task completion degree functions of all resource nodes to obtain task objective functions, adding energy consumption constraint to the task objective functions, and establishing a military countermeasure game decision model; randomly selecting a strategy to form a decision combination, and calculating a benefit value of each resource node for executing the decision combination; carrying out evolution solving on the military countermeasure game decision model according to the individual income value and updating the decision combination; selecting resources of the military countermeasure network according to the updated decision combination; according to the invention, after partial physical nodes in the network information system are damaged, a resource integration scheme is accurately given to recover the core capability of the system, so that the survivability of the network information system is improved.
Description
Technical Field
The invention belongs to the field of network resource allocation, and particularly relates to a method and a system for selecting resources of an evolutionary game network information system.
Background
At present, the selection of the battle resources in China mainly depends on a plan, the plan which is expected to be similar to the current battle field situation is selected, and then the node resources are selected according to the plan to complete the task. With the development of artificial intelligence technology, AI technology is increasingly applied to battlefields, and resource integration by means of a scheme has obvious defects. On one hand, with the mass use of intelligent unmanned equipment, the capacity of the intelligent unmanned equipment is changed through self-learning and is not consistent with the design basis of a plan; on the other hand, the current combat is a combat style with rapid decision and dynamic interference, and the plans are difficult to cover complex future battlefield situations, so that the resource integration is inaccurate and the dynamic adjustment of the resource combination is low in efficiency. Therefore, how to accurately give out a resource integration scheme according to the battlefield situation in the future battlefield of the high dynamic environment is a difficult problem to be solved in the construction of a network information system.
Disclosure of Invention
The invention aims to provide a resource selection method and a system for an evolutionary game network information system, which accurately give out a resource integration scheme after partial physical nodes in the network information system are damaged so as to recover the core capacity of the system and improve the survivability of the network information system.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the first aspect of the invention provides a method for selecting resources of an evolutionary game network information system, which comprises the following steps:
acquiring a decision space in a military countermeasure network, and constructing a task completion degree function in the task execution process of each resource node in the decision space;
superposing task completion degree functions of all resource nodes to obtain task objective functions, adding energy consumption constraint to the task objective functions, and establishing a military countermeasure game decision model;
randomly selecting a strategy to form a decision combination, and calculating a benefit value of each resource node for executing the decision combination; evolving a military countermeasure game decision model according to the individual profit value and updating a decision combination; and selecting resources of the military countermeasure network according to the updated decision combination.
Preferably, the decision space is a surviving set of resource nodes S; the types of the resource nodes in the resource node set S comprise a perception node, an information processing node, a decision node and a fire node.
Preferably, the method for constructing the task completion degree function in the task execution process of the sensing node comprises the following steps:
the sensing node provides a high accuracy sensing range AoH of:
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sense node provides a medium accuracy sense range AoM of:
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sensing node provides a high accuracy sensing range AoL of:
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
in the formula, s i Expressed as resource nodes, S ′ Represented as a collection of resource nodes that perform a task,
minus (-) is expressed as the subtraction of the ranges; ins (-) is expressed as the intersection calculation of the ranges; s is(s) i .cl＝O b The resource node is expressed as a perception node; s is(s) i Do=H is expressed as selecting a high-precision perception value range; s is(s) i Do=M is expressed as a precision perception value range in selection; s is(s) i Do=L is expressed as selecting a low-precision perception value range; x and y representInputting a variable; oo of x And Oo y Represented as the center of the perception range; s is(s) i Ro is denoted as the radius of the sensing range.
Preferably, the method for constructing the task completion degree function in the task execution process of the information processing node comprises the following steps:
the information analysis force Co in the task execution process is as follows:
the task completion degree function of the information processing node is expressed as the following formula:
in the formula, s i .cl＝O r The resource node is represented as an information processing node; s is(s) i Po is expressed as a node analytical force constant; col i Expressed as a lower limit of intelligence analysis capability; cou i Represented as the upper limit of intelligence analysis capability.
Preferably, the expression of the task completion degree function in the task execution process of the decision node is:
the decision force Cd in the task execution process is:
the task completion degree function of the decision node is expressed as the following formula:
in the formula, s i Cl=d represents that the resource node is a decision node; s is(s) i Pd is expressed as a node decision force constant; cdl i Expressed as a lower decision capability limit; cdu i Represented as an upper decision capability limit.
Preferably, the expression of the task completion degree function in the process of executing the task by the fire node is as follows:
the fire node provides a high precision sensing range AaH of:
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
the firepower node provides a middle precision perception range AaM which is as follows:
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
the fire node provides a high precision sensing range AaL of:
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
in the formula, s i Cl=a, the resource node is the sensing node; s is(s) i Da=h is expressed as selecting a high-precision fire value range; s is(s) i Da=m is expressed as the mid-selection precisionA firepower value range; s is(s) i Da=l represents selecting a low-precision fire power range; oa (Oa) x ，Oa y Center coordinates expressed as a sensing range; s is(s) i Ra is expressed as the radius of the fire range.
Preferably, the expression formula of the task objective function is:
in the formula, omega i Representing the desirability of performing a task for the ith resource node.
Preferably, the expression formula of the energy consumption constraint is:
in the formula, R 'is expressed as a fault power supply line set, and R' i Represented as a faulty power supply line; r's' i Rs is expressed as a set of nodes on the faulty power supply line; r's' i REn is expressed as the upper energy consumption limit; s is(s) j En represents the total energy consumption of each resource node.
Preferably, the expression formula of the benefit value of each resource node for executing the decision combination is:
in the formula, T ′ Represented as a decision combination; when the resource node is assembled S ′ Executing decision making combinations T ′ Feasible, marked as T ′ ＝1The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, it is marked as T ′ ＝0。
Preferably, the method for evolving the military countermeasure game decision model and updating the decision combination according to the individual benefit value comprises the following steps:
the resource nodes are used as game individuals to sequentially execute decision combination and then reversely set the decision combination, and the game individuals continue to execute reversely set decision combination;
comparing the profit value in the decision making combination execution process with the profit value in the reverse decision making combination execution process, and updating the profit value into the decision making combination;
after adding a disturbance process with the probability of p, a strategy is selected again randomly to form a new decision combination for evolution iteration, and when the number of evolution iterations reaches a set threshold, the decision combination is output.
Preferably, the decision combination is optimized by means of a monte carlo remorse value minimization algorithm.
The second aspect of the present invention provides a control system for resource selection of an evolutionary game network information system, comprising:
the acquisition module is used for acquiring a decision space in the military countermeasure network;
the model construction module is used for constructing a task completion degree function in the task execution process of each resource node in the decision space; superposing task completion degree functions of all resource nodes to obtain task objective functions, adding energy consumption constraint to the task objective functions, and establishing a military countermeasure game decision model;
the evolution module is used for randomly selecting strategies to form decision combinations and calculating the benefit value of each resource node for executing the decision combinations; evolving a military countermeasure game decision model according to the individual profit value and updating a decision combination;
and the execution module is used for selecting resources of the military countermeasure network according to the updated decision combination.
Compared with the prior art, the invention has the beneficial effects that:
the invention randomly selects strategies to form decision combinations, and calculates the profit value of each resource node for executing the decision combinations; evolving a military countermeasure game decision model according to the individual profit value and updating a decision combination; selecting resources of the military countermeasure network according to the updated decision combination; according to the invention, after partial physical nodes in the network information system are damaged, a resource integration scheme is accurately given to recover the core capability of the system, so that the survivability of the network information system is improved.
Drawings
FIG. 1 is a flowchart of a method for selecting resources of an evolutionary gaming network information system provided by an embodiment of the present invention;
FIG. 2 is a graph of the iteration number of the GA algorithm versus the present invention provided in an embodiment of the present invention;
FIG. 3 is a graph of iteration count versus disturbance limit provided by an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
As shown in fig. 1, this embodiment provides a method for selecting resources of an evolutionary game network information system, including:
acquiring a decision space in a military countermeasure network, wherein the decision space is a surviving resource node set S; the types of the resource nodes in the resource node set S comprise a sensing node, an information processing node, a decision node and a fire node; as shown in table 1;
TABLE 1 node resource attributes
The method for constructing the task completion degree function in the task execution process of each resource node in the decision space comprises the following steps:
the sensing node provides a high accuracy sensing range AoH of:
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sense node provides a medium accuracy sense range AoM of:
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sensing node provides a high accuracy sensing range AoL of:
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
in the formula, s i Expressed as resource nodes, S ′ Represented as a collection of resource nodes that perform a task,
minus (-) is expressed as the subtraction of the ranges; ins (-) is expressed as the intersection calculation of the ranges; s is(s) i .cl＝O b The resource node is expressed as a perception node; s is(s) i Do=H is expressed as selecting a high-precision perception value range; s is(s) i Do=M is expressed as a precision perception value range in selection; s is(s) i Do=L tableShown as selecting a low precision perceptual value range; x and y represent input variables; oo of x And Oo y Represented as the center of the perception range; s is(s) i Ro is denoted as the radius of the sensing range.
The method for constructing the task completion degree function in the task execution process of the information processing node comprises the following steps:
the information analysis force Co in the task execution process is as follows:
the task completion degree function of the information processing node is expressed as the following formula:
in the formula, s i .cl＝O r The resource node is represented as an information processing node; s is(s) i Po is expressed as a node analytical force constant; col i Expressed as a lower limit of intelligence analysis capability; cou i Represented as the upper limit of intelligence analysis capability.
The expression of the task completion degree function in the task execution process of the decision node is as follows:
the decision force Cd in the task execution process is:
the task completion degree function of the decision node is expressed as the following formula:
in the formula, s i Cl=d represents that the resource node is a decision node; s is(s) i Pd is expressed as a node decision force constant; cdl i Expressed as a lower decision capability limit; cdu i Represented as an upper decision capability limit.
The expression of the task completion degree function in the process of executing the task by the fire node is as follows:
the fire node provides a high precision sensing range AaH of:
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
the firepower node provides a middle precision perception range AaM which is as follows:
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
the fire node provides a high precision sensing range AaL of:
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
in the formula, s i Cl=a, the resource node is the sensing node; s is(s) i Da=h is expressed as selecting a high-precision fire value range; s is(s) i Da=m representsSelecting a medium-precision firepower value range; s is(s) i Da=l represents selecting a low-precision fire power range; oa (Oa) x ，Oa y Center coordinates expressed as a sensing range; s is(s) i Ra is expressed as the radius of the fire range.
Superposing task completion degree functions of all the resource nodes to obtain task objective functions, wherein the expression formula of the task objective functions is as follows:
in the formula, omega i Representing the desirability of performing a task for the ith resource node.
Adding energy consumption constraint to the task objective function to establish a military countermeasure game decision model; the expression formula of the energy consumption constraint is as follows:
in the formula, R 'is expressed as a fault power supply line set, and R' i Represented as a faulty power supply line; r's' i Rs is expressed as a set of nodes on the faulty power supply line; r's' i REn is expressed as the upper energy consumption limit; the method comprises the steps of carrying out a first treatment on the surface of the s is(s) j En represents the total energy consumption of each resource node.
The expression formula of the military countermeasure game decision model is as follows:
randomly selecting a strategy to form a decision combination, and calculating a benefit value of each resource node for executing the decision combination; the expression formula of the benefit value of each resource node executing the decision combination is as follows:
in the formula, T ′ Represented as a decision combination; when the resource node is assembled S ′ Executing decision making combinations T ′ Feasible, marked as T ′ =1; otherwise, it is marked as T ′ ＝0。
The method for evolving the military countermeasure game decision model and updating the decision combination according to the individual profit value comprises the following steps:
the resource nodes are used as game individuals to sequentially execute decision combination and then reversely set the decision combination, and the game individuals continue to execute reversely set decision combination;
comparing the profit value in the decision making combination execution process with the profit value in the reverse decision making combination execution process, and updating the profit value into the decision making combination;
after adding a disturbance process with the probability of p, re-randomly selecting a strategy to form a new decision combination for evolution iteration, and optimizing the decision combination through a Monte Carlo regret minimization algorithm; outputting a decision combination when the evolution iteration number reaches a set threshold; and selecting resources of the military countermeasure network according to the updated decision combination.
To ensure computational efficiency, the algorithm has two evolution rounds for each generation of evolution. The purpose of the algorithm is to search for a subset that maximizes the objective function, and the strategy combination corresponding to the nash equalization that is not achieved in a certain situation in 2 rounds can be evolved from other initial strategies.
Simulation test
According to the invention, simulation experiments are carried out on a Netlogo platform. And constructing data sets of related information of four types of equipment, namely 80 radars, an information analysis system, a decision system and a missile, and screening attribute values in the table 1 from the data sets to form a node resource list.
Describing tasks according to requirements, and constructing tasks according to constraint conditions added by lines, so that the number of nodes required for the tasks to be completely completed is about 25, namely 68.75% redundancy of node resources is set.
The experimental parameters are set to pr=0.4 to simulate a physical node loss event, the disturbance probability p=0.04 is set for the EGA-RO algorithm, the crossover probability pc=0.75 and the variation probability pm=0.01 are set for the GA, and the termination condition is set to be evolution or evolution generation number to 200 generations.
The experimental result can obviously show that the task completion degree of the integrated scheme calculated by the EGA-RO algorithm is obviously higher than that of the GA algorithm to more reliably verify the effect of the resource optimization algorithm, and 36 groups of comparison tests are carried out under different damage probabilities, disturbance probabilities, crossover probabilities and variation probabilities to obtain data as shown in Table 2; the experimental result can obviously show that the task completion degree of the integration scheme required by the EGA-RO algorithm is obviously higher than that of the GA algorithm to more reliably verify the effect of the resource optimization algorithm
TABLE 2 comparison of EGA-RO algorithm with GA algorithm under different parameters
As shown in fig. 2, for 10 sets of data with the same effect obtained by the two algorithms, the experiment further compares the algebra of the optimal value corresponding scheme; according to the analysis of the experimental data, the invention provides that the EGA-RO algorithm has a proportion of 72.2 percent to obtain a solution with better effect than the method based on the basic genetic algorithm (GA algorithm), and when the same solution is obtained, the EGA-RO algorithm has an optimal scheme average algebra of 38, and the genetic algorithm has an optimal scheme average algebra of 95.8. The analysis results can show that the resource optimization method provided by the invention has obvious advantages over the currently common optimization method based on the genetic algorithm when the problems researched by the invention are solved.
Secondly, according to the improvement of adding disturbance process limitation to the algorithm, the experiment compares the algorithm with the algorithm without disturbance process limitation. The experiment simulates a damage event according to the damage probability pr=0.4 and pr=0.5, the resource integration scheme is solved according to the same disturbance probability p=0.04 under the same damage result and the same initial strategy, and the two algorithms are repeated for 10 times.
As shown in fig. 3, when the probability of corruption is pr=0.4, there is a 50% increase in the disturbance-limited resource preference algorithm gm below the no disturbance limit. When the probability of corruption increases to pr=0.5, there is a 70% increase in the disturbance limiting resource preference algorithm gm below the no disturbance limit. When pr=0.4, the gm of the presence/absence disturbance limit is 29.7 and 46.6 respectively, and when pr=0.4, is 23.5 and 40.1 respectively, according to analysis of the experimental data, it can be seen that the addition of the disturbance limit can lead gm to be 17 in average in advance, that is, the addition of the disturbance limit effectively improves algorithm efficiency.
Example two
The present embodiment provides a control system for resource selection of an evolutionary game network information system, where the control system provided in the present embodiment may be applied to the control method described in the first embodiment, and the control system includes:
the acquisition module is used for acquiring a decision space in the military countermeasure network;
the model construction module is used for constructing a task completion degree function in the task execution process of each resource node in the decision space; superposing task completion degree functions of all resource nodes to obtain task objective functions, adding energy consumption constraint to the task objective functions, and establishing a military countermeasure game decision model;
the evolution module is used for randomly selecting strategies to form decision combinations and calculating the benefit value of each resource node for executing the decision combinations; evolving a military countermeasure game decision model according to the individual profit value and updating a decision combination;
and the execution module is used for selecting resources of the military countermeasure network according to the updated decision combination.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (6)
1. The method for selecting the resources of the evolution game network information system is characterized by comprising the following steps of:
acquiring a decision space in a military countermeasure network, wherein the decision space is a surviving resource node set S; the types of the resource nodes in the resource node set S comprise a sensing node, an information processing node, a decision node and a fire node; constructing a task completion degree function in the task execution process of each resource node in the decision space;
the specific process of constructing the task completion degree function in the task execution process of the sensing node is as follows:
the sensing node provides a high accuracy sensing range AoH of:
s.t.(xs i .Oo x ) 2 +(y-s i .Oo y ) 2 ≤s i .o 2
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sense node provides a medium accuracy sense range AoM of:
s.t.(xs i .Oo x ) 2 +(y-s i .Oo y ) 2 ≤s i .o 2
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sensing node provides a high accuracy sensing range AoL of:
s.t.(xs i .Oo x ) 2 +(y-s i .Oo y ) 2 ≤s i .o 2
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
in the formula, s i Expressed as resource nodes, S ′ Represented as a collection of resource nodes that perform a task,
minus is expressed as a range; ins.is expressed as the intersection calculation of the ranges; s is(s) i .cl＝O b The resource node is expressed as a perception node; s is(s) i Do=H is expressed as selecting a high-precision perception value range; s is(s) i Do=M is expressed as a precision perception value range in selection; s is(s) i Do=L is expressed as selecting a low-precision perception value range; x and y represent input variables; oo of x And Oo y Represented as the center of the perception range; s is(s) i O is expressed as a perceived range radius;
superposing task completion degree functions of all resource nodes to obtain task objective functions, adding energy consumption constraint to the task objective functions, and establishing a military countermeasure game decision model;
the expression formula of the energy consumption constraint is as follows:
in the formula, R ′ Represented as a faulty power supply line set, r i ′ Represented as a faulty power supply line; r is (r) i ′ S is expressed as a node set on a fault power supply line; r is (r) i ′ En is expressed as the upper energy consumption limit; s is(s) j N represents the total energy consumption of each resource node;
the random selection strategy forms a decision combination, and the profit value of each resource node executing the decision combination is calculated, wherein the expression formula is as follows:
in the formula, T ′ Represented as a decision combination; when the resource node is assembled S ′ Executing decision making combinations T ′ Feasible, marked as T ′ =1; otherwise, it is marked as T ′ ＝0；
Evolving a military countermeasure game decision model according to the individual profit value and updating a decision combination; and selecting resources of the military countermeasure network according to the updated decision combination.
2. The method for selecting resources of an evolutionary gaming network information system of claim 1, wherein the method for constructing a task completion function in the process of executing tasks by the intelligence processing node comprises:
the information analysis force Co in the task execution process is as follows:
the task completion degree function of the information processing node is expressed as the following formula:
in the formula, s i .cl＝O r The resource node is represented as an information processing node; s is(s) i O is denoted as node analysis force constant; col i Expressed as a lower limit of intelligence analysis capability; cou i Represented as the upper limit of intelligence analysis capability.
3. The method for selecting resources of an evolutionary gaming network information system of claim 2, wherein the decision node performs the task with a task completion function expressed as:
the decision force Cd in the task execution process is:
the task completion degree function of the decision node is expressed as the following formula:
in the formula, s i Cl=d represents that the resource node is a decision node; s is(s) i D is expressed as a node decision force constant; cdl i Expressed as a lower decision capability limit; cdu i Represented as an upper decision capability limit.
4. The method for selecting resources of an evolutionary gaming network information system of claim 1, wherein the expression of the task completion function in the process of executing tasks by fire nodes is:
the fire node provides a high precision sensing range AaH of:
s.t.(xs i .Oa x ) 2 +(y-s i .Oa y ) 2 ≤s i .a 2
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
the firepower node provides a middle precision perception range AaM which is as follows:
s.t.(xs i .Oa x ) 2 +(y-s i .Oa y ) 2 ≤s i .a 2
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
the fire node provides a high precision sensing range AaL of:
s.t.(xs i .Oa x ) 2 +(y-s i .Oa y ) 2 ≤s i .a 2
the completion degree of the fire node executing the high-precision monitoring task is expressed as the following formula:
in the formula, s i Cl=a, the resource node is the sensing node; s is(s) i Da=h is expressed as selecting a high-precision fire value range; s is(s) i Da=m represents the range of the selected medium-precision fire power values; s is(s) i Da=l represents selecting a low-precision fire power range; oa (Oa) x ，Oa y Center coordinates expressed as a sensing range; s is(s) i A is denoted as the fire range radius.
5. The method for resource selection of an evolutionary gaming network information system of claim 1, wherein the method for evolving the military countermeasure gaming decision model and updating the decision combination based on the individual benefit values comprises:
the resource nodes are used as game individuals to sequentially execute decision combination and then reversely set the decision combination, and the game individuals continue to execute reversely set decision combination;
comparing the profit value in the decision making combination execution process with the profit value in the reverse decision making combination execution process, and updating the profit value into the decision making combination;
after adding a disturbance process with the probability of p, a strategy is selected again randomly to form a new decision combination for evolution iteration, and when the number of evolution iterations reaches a set threshold, the decision combination is output.
6. A control system for resource selection of an evolving gaming network information system, comprising:
the acquisition module is used for acquiring a decision space in the military countermeasure network; the decision space is a surviving resource node set S; the types of the resource nodes in the resource node set S comprise a sensing node, an information processing node, a decision node and a fire node;
the model construction module is used for constructing a task completion degree function in the task execution process of each resource node in the decision space; superposing task completion degree functions of all resource nodes to obtain task objective functions, adding energy consumption constraint to the task objective functions, and establishing a military countermeasure game decision model; the expression formula of the energy consumption constraint is as follows:
in the formula, R ′ Represented as a faulty power supply line set, r i ′ Represented as a faulty power supply line; r is (r) i ′ S is expressed as a node set on a fault power supply line; r is (r) i ′ EE is expressed as the upper energy consumption limit; s is(s) j N represents the total energy consumption of each resource node;
the specific process of constructing the task completion degree function in the task execution process of the sensing node is as follows:
the sensing node provides a high accuracy sensing range AoH of:
s.t.(xs i .Oo x ) 2 +(y-s i .Oo y ) 2 ≤s i .o 2
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sense node provides a medium accuracy sense range AoM of:
s.t.(xs i .Oo x ) 2 +(y-s i .Oo y ) 2 ≤s i .o 2
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
the sensing node provides a high accuracy sensing range AoL of:
s.t.(xs i .Oo x ) 2 +(y-s i .Oo y ) 2 ≤s i .o 2
the expression formula of the completion degree of the sensing node executing the high-precision monitoring task is as follows:
in the formula, s i Expressed as resource nodes, S ′ Represented as a collection of resource nodes that perform a task,
minus is expressed as a range; ins.is expressed as the intersection calculation of the ranges; s is(s) i .cl＝O b The resource node is expressed as a perception node; s is(s) i Do=H is expressed as selecting a high-precision perception value range; s is(s) i Do=M is expressed as a precision perception value range in selection; s is(s) i Do=L is expressed as selecting a low-precision perception value range; x and y represent inputsA variable; oo of x And Oo y Represented as the center of the perception range; s is(s) i O is expressed as a perceived range radius;
the evolution module is used for randomly selecting strategies to form decision combinations and calculating the benefit value of each resource node for executing the decision combinations; evolving a military countermeasure game decision model according to the individual profit value and updating a decision combination;
the profit value expression formula for calculating the decision combination executed by each resource node is as follows:
in the formula, T ′ Represented as a decision combination; when the resource node is assembled S ′ Executing decision making combinations T ′ Feasible, marked as T ′ =1; otherwise, it is marked as T ′ ＝0；
And the execution module is used for selecting resources of the military countermeasure network according to the updated decision combination.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210615542.4A CN115022192B (en) | 2022-06-01 | 2022-06-01 | Resource selection method and system for evolutionary game network information system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210615542.4A CN115022192B (en) | 2022-06-01 | 2022-06-01 | Resource selection method and system for evolutionary game network information system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115022192A CN115022192A (en) | 2022-09-06 |
| CN115022192B true CN115022192B (en) | 2023-05-23 |
Family
ID=83071068
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210615542.4A Active CN115022192B (en) | 2022-06-01 | 2022-06-01 | Resource selection method and system for evolutionary game network information system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115022192B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108833401A (en) * | 2018-06-11 | 2018-11-16 | 中国人民解放军战略支援部队信息工程大学 | Network active defensive strategy choosing method and device based on Bayes's evolutionary Game |
| CN112329348A (en) * | 2020-11-06 | 2021-02-05 | 东北大学 | Intelligent decision-making method for military countermeasure game under incomplete information condition |
-
2022
- 2022-06-01 CN CN202210615542.4A patent/CN115022192B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108833401A (en) * | 2018-06-11 | 2018-11-16 | 中国人民解放军战略支援部队信息工程大学 | Network active defensive strategy choosing method and device based on Bayes's evolutionary Game |
| CN112329348A (en) * | 2020-11-06 | 2021-02-05 | 东北大学 | Intelligent decision-making method for military countermeasure game under incomplete information condition |
Non-Patent Citations (2)
| Title |
|---|
| System Architecture:Strategy and Product Development for Complex Systems;Crawley E, etc.;《Hoboken: Pearson Higher Education》;全文 * |
| 全军网络信息体系能力演化分析框架研究;张婷婷等;《指挥与控制学报》;第3卷(第3期);第230-235页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115022192A (en) | 2022-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Differential evolution based on covariance matrix learning and bimodal distribution parameter setting | |
| Jin et al. | A systems approach to evolutionary multiobjective structural optimization and beyond | |
| CN112491818A (en) | Power grid transmission line defense method based on multi-agent deep reinforcement learning | |
| CN109858798B (en) | Power grid investment decision modeling method and device for correlating transformation measures with voltage indexes | |
| CN109344969B (en) | Neural network system, training method thereof, and computer-readable medium | |
| Guo et al. | Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities. | |
| CN111695754A (en) | Electric power Internet of things information security risk assessment method and device | |
| CN111461284A (en) | Data discretization method, device, equipment and medium | |
| Jia et al. | A distributed cooperative co-evolutionary CMA evolution strategy for global optimization of large-scale overlapping problems | |
| CN112685841B (en) | Finite element modeling and correcting method and system for structure with connection relation | |
| CN111192158A (en) | Transformer substation daily load curve similarity matching method based on deep learning | |
| CN115022192B (en) | Resource selection method and system for evolutionary game network information system | |
| CN117170980B (en) | Early warning method, device, equipment and storage medium for server hardware abnormality | |
| Farahani et al. | A hybrid meta-heuristic optimization algorithm based on SFLA | |
| CN112434733B (en) | Small-sample hard disk fault data generation method, storage medium and computing device | |
| CN112712178A (en) | Bayesian network structure learning method and system based on genetic algorithm | |
| CN112633559B (en) | Social relationship prediction method and system based on dynamic graph convolutional neural network | |
| CN116527411B (en) | Data security intelligent protection model construction method and device and collaboration platform | |
| Kang et al. | Multi-objective evolutionary optimization of 3D differentiated sensor network deployment | |
| CN117435308B (en) | Modelica model simulation method and system based on parallel computing algorithm | |
| CN115983719B (en) | Training method and system for software comprehensive quality evaluation model | |
| Chang | Convergence Analysis of Particle Swarm Optimization via Illustration Styles | |
| CN118014316A (en) | Asexual genetic algorithm-based blocking flow shop scheduling method and electronic equipment | |
| CN112632732A (en) | Method and system for evaluating system vulnerability | |
| CN115599659A (en) | Parallel program test path selection method based on evolutionary algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |