CN115619105B - Dynamic evolution system capacity analysis method and system based on simulation big data - Google Patents
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Abstract
The invention discloses a dynamic evolution system capacity analysis method and a system based on simulation big data, wherein the method comprises the following steps: establishing a super network model of complex information system combat; constructing a corresponding initial index set based on the super network model; establishing a network information index system of a top-level index; establishing a system elasticity index system of a complex system; constructing a dynamic index network, and performing time evolution analysis on the indexes; calculating complex system capacity based on time sequence; the system comprises a simulation data management module, an index management module, a network information system index system construction module, an index analysis module, a model construction module and a system capacity analysis module. The invention can clearly describe the overall appearance of the system; the method is optimized on a dynamic evolution system capacity analysis method, and the obtained result is more suitable for the characteristics of complex system activities and is more reliable and more pertinent than the traditional method.
Description
Technical Field
The invention relates to the field of modeling and analysis of network information systems, in particular to a dynamic evolution system capacity analysis method and system based on simulation big data.
Background
With the rapid development of the modern technology and the internet technology, the modern war form is greatly transformed, and the future war is a war with network and information as centers and is a representation of global combined combat; war becomes more complex, and the antagonistic features between systems are increasingly prominent; how to describe the characteristics of complexity, dynamics, emergence and the like of the system is a problem which needs to be solved. The network information system is a complex nonlinear system, the structure and the relation of the system are complex and evolve, and the component systems are interdependent and associated; the combat effectiveness of the system is a key index for measuring the combat capability of the system, how to analyze the system capability of the network information system and how to give out a corresponding construction scheme according to the analysis result is a key problem to be solved in the research of the network information system.
The data in the real military field has the problems of high security level, complex sources, difficult sharing and the like, so that the persuasion of related researches is greatly reduced. The simulation data generated by deduction through the simulation system not only comprises the process of system countermeasure and result data, but also comprises the combat command data of combat commanders and the like, and the data can well embody the complexity rule of war. Therefore, it is particularly important to research a dynamic evolution system capacity analysis method and system based on simulation big data.
Disclosure of Invention
The invention aims to: the invention aims to provide a dynamic evolution system capacity analysis method and system based on simulation big data.
The technical scheme is as follows: the invention discloses a dynamic evolution system capacity analysis method based on simulation big data, which comprises the following steps:
step (1) establishing a super network model of complex information system combat;
step (2) constructing a corresponding initial index set based on the super network model;
step (3) establishing a network information index system of the top level index;
step (4) establishing a system elasticity index system of the complex system;
step (5) constructing a dynamic index network, and carrying out time evolution analysis on the indexes;
step (6) of calculating complex system capacity based on time sequence,
the step (4) comprises the steps of aiming at the influence of network system elasticity and the self-characteristics of each subsystem unit on system elasticity along with time change, which are exhibited by the dynamic evolution of a complex system, synthesizing the characteristics of the complex system and the system countermeasure process, describing the complex system elasticity process through the system capacity dynamic change of the complex system, and constructing a system elasticity index system of the complex system;
the step (5) comprises the following steps:
(51) Determining index weight values corresponding to nodes of each layer of system elasticity of the complex system according to the super network model and the system elasticity index network of the complex system;
(52) The system elastic command decision capability is influenced by risk perception capability and communication capability, the system elastic recovery reconstruction capability is influenced by command decision capability and damage striking capability, and the evolution rules of the system in different stages are found based on the evolution of command decision capability nodes and damage striking capability nodes and the evolution of the continuous edge relationship, and the characteristic relationship of the evolution of different types of indexes along with time is analyzed;
(53) Carrying out correlation analysis on multiple indexes to find out the correlation among the indexes;
the step (6) comprises the following steps:
(61) To be used for
Time of day is for example->
Representing the sensing capacity of the system,/->
Representing the communication capability of the system,
Indicating the control capability of the system, +.>
Representing the striking power of the system;
(62) For a pair of
Time->
Carrying out importance ranking;
(63) The attack task, the attack intensity, the attack position and the hit probability index influence the effectiveness value of the hit capability, the index time influences each other, the attack task influences the attack intensity and the attack position, and the attack position is connected with the hit probability;
(64) Based on the indexes, setting a threshold value according to specific conditions by utilizing key index data extracted from an initial index set, extracting key indexes which have influence on system capacity and exceed the threshold value from a plurality of influence indexes, and carrying out normalization processing on the data by adopting an extremum method to obtain normalized data
，/>
Representing test data;
(65) The entropy weight method is adopted to calculate the weight of the processed index, and the result is expressed as:
(66) Based on the following calculation
Moment attack ability efficacy value->
：
Similar calculation
Other capability efficacy values at the moment;
(67) Dynamic evolution system
The system capacity at the moment is expressed as:
wherein 
The weight values representing the sensing, communication, control and striking capabilities of the system are in the range of (0, 1), and the following conditions are satisfied:
(68) The comprehensive system capacity of the dynamic evolution of the whole stage is expressed as follows:
wherein ,
weight values representing different capacities in dynamic evolution process of system, and
。
further, the step (1) includes constructing a four-layer super network model of a sensing network, a communication network, a command network and a striking network according to the structural relation of the complex system, and the super network model is expressed as:
wherein ,
representing a sensor network->
Representing a communication network,/->
Indicating command net +.>
Indicates a striking net->
Representing the connective relationship between nodes, +.>
Representation->
A sub-network; />
Each movable unit entity comprises 4 kinds of nodes, namely sensing nodes
Communication node->
Finger control node->
And hit node->
The interactive relationship between the active unit entities is a continuous edge, and the continuous edge relationship comprises a communication relationship
Further, the step (2) includes the steps of:
(21) Preprocessing basic data, including cleaning, reducing and integrating the data;
(22) The obtained initial index set comprises detection reconnaissance indexes, communication indexes, command control indexes and damage hit indexes.
Further, the step (3) comprises selecting typical indexes capable of reflecting Guan Jianneng force, and constructing a networked index system comprising indexes of elasticity, linkage, robustness, autonomy, flexibility and adaptability.
Further, the dynamic evolution system capacity analysis system based on the simulation big data comprises a simulation data management module, an index management module, a network information system index system construction module, an index analysis module, an analysis model construction module and a system capacity analysis module of a network information system;
the simulation data management module is used for carrying out data mining from mass simulation data and then carrying out data importing and storing;
the index management module is used for realizing the functions of adding, editing, deleting and inquiring indexes;
the network information system index system construction module is used for analyzing the characteristics of requirements and analysis objects and constructing a system elastic network index system based on a network information system;
the index analysis module is used for index correlation analysis and time evolution analysis, extracting key data and constructing a dynamic index network based on a network information system;
the analysis model construction module is used for calculating the weight of the index data and the weight of the system unit capacity;
the system capacity analysis module of the network information system is used for analyzing the system capacity of the network information system based on the OODA ring theory by utilizing the network index system and the system unit capacity weight to obtain an analysis result.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: based on the sensing node, the communication node, the command node and the hitting node, a four-layer super network model is established through a corresponding network, so that the overall appearance of the system can be clearly described; by establishing an index system which is changed into a top index from instant optimization and agility, constructing an elastic networked index system based on a complex system, constructing a dynamic index network according to index time sequence correlation analysis, and constructing an OODA loop and adopting a weighted average method, the analysis of the system capacity of dynamic evolution is realized, and assistance can be provided for subsequent system contribution degree analysis, sensitivity analysis, system gravity center searching and system weak points searching.
Drawings
FIG. 1 is a flow chart of a dynamic evolution system capability analysis method;
FIG. 2 is a schematic diagram of a super network model of a complex system;
FIG. 3 is a schematic diagram of a system capacity index system of a complex system;
FIG. 4 is a schematic diagram of a system elasticity index system of a complex system;
FIG. 5 is a flow chart of dynamic index network construction based on time series;
FIG. 6 is a diagram of a system loop construction process based on an OODA loop model;
FIG. 7 is a diagram of a dynamic evolution system capability analysis system framework.
Description of the embodiments
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in FIG. 1, the dynamic evolution system capacity analysis method based on simulation big data comprises the following steps:
(1) Establishing a super network model of complex information system combat;
(2) Constructing a corresponding initial index set based on the super network model;
(3) Establishing a network information index system which takes instant aggregation, optimization and agility as top-level indexes;
(4) Establishing a system elasticity index system of a complex system;
(5) Constructing a dynamic index network, and performing time evolution analysis on the indexes;
(6) The complex system capabilities based on time series are calculated.
The step (1) of establishing a super network model for complex information system combat specifically comprises the following steps: according to the structural relation of a complex system, a four-layer super network model of a sensing network, a communication network, a command network and a hitting network is constructed, as shown in fig. 2, the comprehensive system network is a network in the comprehensive network of the sensing network, the communication network, the command network and the hitting network, and each movable unit entity can be divided into 4 types of nodes which are respectively a sensing node S, a communication node C, a command node D and a hitting node A; the interaction relation between the active unit entities is a continuous edge, which specifically comprises: the communication relationship represents the information transmission relationship of the communication node to the communication (sensing, controlling and beating) node; the information sharing relationship represents an information sharing relationship between the sensing nodes; the information support relationship indicates that the sensing node transmits target information to the hit node or the decision node; the command control relation represents that the command control node transmits command control information to the hitting node, the subordinate command control node and the sensing node; the collaborative decision relationship represents collaborative decision for completing the same task through information sharing among the command nodes; the state reporting relation represents that the hit node (sensing node and finger control node) reports the state information of the hit node to the upper finger control node; the reconnaissance relation means that the sensing node carries out reconnaissance monitoring on the target and acquires the related information of the target.
Formalized description of the super network model is:
； wherein ,/>
Representing a sensor network->
Representing a communication network,/->
Indicating command net +.>
Indicates a striking net->
, wherein />
Representation->
A sub-network; />
Representing class 4 nodes, the specific description is shown in table 1; />
The connection relationship between nodes is shown in table 2.
Table 1: node type of complex system
Table 2: connection relationship between nodes of complex system
The system capacity analysis is to mine key indexes affecting the system capacity from mass data, firstly, the relation between the mass data and the evaluation indexes is to be established, and the step (2) of constructing a corresponding initial index set based on the super network model comprises the following steps: on the premise of reliable data and meeting evaluation requirements, the method comprises the steps of including indexes as much as possible, wherein the indexes comprise detection reconnaissance data, communication data, command control data and hit damage data; preprocessing basic data, including cleaning, reducing and integrating log data, instruction data and the like mainly including data; the initial index set obtained in the above manner includes: detecting reconnaissance indexes, communication indexes, command control indexes and hit damage indexes.
Step (3) establishes a networked index system which is changed into top-level capability by instant optimization and agility, selects typical indexes capable of reflecting Guan Jianneng force, and constructs the networked index system which comprises indexes such as elasticity, linkage, robustness, autonomy, flexibility, adaptability and the like around the top-level capability of the complex system due to wide types of related influence indexes and complex association relationship among indexes, as shown in fig. 3.
Step (4) a system elasticity index system of the complex system is established, aiming at the influence of the network system elasticity emerging from the dynamic evolution of the complex system and the self-characteristics of each subsystem unit on the system elasticity along with the time change, the characteristics of the complex system and the system countermeasure process are synthesized, the complex system elasticity process can be described through the dynamic change of the system capacity of the complex system, and the complex system elasticity is the result of the dynamic evolution of the network structure and is influenced by the characteristics of the subsystem; thus, the elastic process of the system is described by the change of the system capacity of the complex system,
according to the characteristics of the complex system and the process analysis of system countermeasure, the elastic process of the complex system is divided into 5 stages of information reconnaissance, situation judgment, command control, damage striking, recovery and reconstruction.
As shown in fig. 4, a system elastic networking index system of a complex system is established,
because of a plurality of influence factors of the elasticity of the complex system, reliable test data are needed for analysis, and data processing is carried out based on index data extracted by a super network; for example, in the evolution process of a system, the command decision-making capability of the system elasticity is influenced by risk perception capability and communication capability; the recovery and reconstruction capability of the system elasticity is influenced by the command decision capability and the damage striking capability, so that the analysis of the system elasticity process can reflect the dynamic evolution of the whole system capability laterally.
As shown in fig. 5, the detection reconnaissance data, communication data, command control data and striking damage data are further preprocessed, wherein the data processing includes log data, instruction data and the like, and a basic index system of system capacity is formed after the data processing.
And (5) constructing a dynamic index network to perform time evolution analysis on indexes, wherein the method comprises the following specific steps of:
carrying out correlation analysis among indexes by using a maximum information interaction algorithm, and searching for the correlation among different indexes; the relationship between indexes is continuously changed along with time evolution, so that a change rule between indexes evolving along with time can be obtained, and finally, a dynamic index network evolving along with time is obtained, wherein the specific algorithm comprises the following steps:
firstly, calculating mutual information and solving a maximum mutual information value;
dividing index pairs
A kind of ∈9 of the coordinate plane>
Go->
Column grid->
Probability Density->
Is the ratio of the number of sample points to the total number of samples in the index. Grid->
Is used for representing index matchingThe concentration degree of the present invention is that,
correlation strength between index pairs under grid h division condition
Is that
Secondly, carrying out normalization processing on the maximum mutual information value;
assume that the grid has multiple dividing methods, namely
Thus define the H mutual information characteristic value as
Finally, the maximum value of mutual information under different scales is selected as the MIC value, which can be expressed as
Calculating the maximum information coefficient as
Index pair
The maximum information coefficient is the maximum value in the characteristic value matrix, and the closer the numerical value is to 1, the stronger the association relation among indexes is indicated;
determining index weight values corresponding to nodes of each layer of system elasticity of a complex system according to a system elasticity index network established based on a super network model and the complex system;
according to the evolution of the nodes and the evolution of the edge connection relation, the characteristic relation of the evolution of different types of indexes along with time is analyzed, so that the evolution rules of the system at different stages can be found; and meanwhile, correlation analysis is carried out on multiple indexes, so that the correlation among the indexes can be found.
The step (6) of calculating the system capacity of the complex system based on the time sequence comprises the following detailed steps:
the system activity is divided into several important stages: 4 stages of reconnaissance perception, information communication, command decision and damage striking are performed, as shown in fig. 6, an OODA loop is constructed based on a network, the system capacities of different stages are calculated by using a weighted average method, and the system capacities of corresponding complex systems are calculated for different stages, and specifically include:
the system capacity for calculating dynamic evolution at different stages is respectively as follows:
wherein 
The weight values representing the sensing, communication, control and striking capabilities of the different stage systems are in the range of (0, 1).
The comprehensive capacity of the whole stage complex system is calculated as follows:
wherein ,
weight values representing different capacities in dynamic evolution process of system, and
。
to analyze the system capacity of a complex system which dynamically evolves, the following assumptions are proposed based on the OODA theory:
as shown in fig. 6, based on the idea of the OODA loop model, the complex system activity process can be regarded as a loop process of target perception, information communication, command decision and damage striking, and it is assumed that the system activity process is divided into four processes:
stage hypothesis the perceptual energy at this timeForce is->
And->
；/>
The phase assumes that the communication capacity at this time is +.>
And->
；/>
The phase assumes that the command decision capability at this time is +.>
And->
；
The stage assumes that the striking power at this time is +.>
And->
This hypothesis shows that the system capacity of a complex system is dynamically changing over time at different stages, which is consistent with the actual situation, provided that +.>
During the whole complex system activity, the construction process of one loop is discussed in time,
based on the above assumptions, there are the following formulas:
through the analysis, the whole process of the complex system activity is analyzed for 4 stages of target perception, information communication, command decision and damage strike respectively, an OODA loop is constructed based on a network, the comprehensive capacity of the system is analyzed by adopting a weighted average method, and key factors influencing the capacity of the system are obtained, but the relevance among indexes is not described, for example, the command decision capacity is subjected to interaction of the information communication capacity, so that the mutual relationship between the information communication capacity and the command decision is considered in the command decision stage. The research thought provided by the invention optimizes the dynamic evolution system capacity assessment method, and the obtained assessment result is more suitable for the characteristics of complex system activities, and is more reliable and more pertinent than the traditional assessment method.
As shown in FIG. 7, the dynamic evolution system capability analysis system based on simulation big data comprises a simulation data management module, an index management module, a network information system index system construction module, an index analysis module, an analysis model construction module and a system capability analysis module of a network information system;
the simulation data management module is used for carrying out data mining from mass simulation data and then carrying out data importing and storing. Classifying the simulation data, and on the premise of reliable data and meeting analysis requirements, the simulation data contains index data as much as possible, and specifically comprises detection investigation data, communication data, command control data and damage hitting data; preprocessing the data, including cleaning, reducing and integrating log data, instruction data and the like mainly including data; the obtaining of the initial index data set in the above manner includes: detecting investigation type index data, communication type index data, command control type index data and hit damage type index data; and finishing the importing and storing of the index data.
The index management module is used for realizing the functions of adding, editing, deleting, inquiring and the like of the index.
The network information system index system construction module is used for analyzing the characteristics of requirements and analysis objects and constructing a system elastic network index system based on the network information system. The system elasticity of the network information system emerging from the dynamic evolution of the network information system is influenced by the change of the characteristics of each subsystem unit along with time. The characteristics of the network information system and the system countermeasure process are integrated, the elastic process of the network information system can be described through the dynamic change of the system capacity of the network information system, and a system elastic index system of the network information system is constructed; obtaining quantitative index data of the elasticity of a network information system, wherein the quantitative index data comprises detection investigation data, communication data, command control data and damage hitting data; and constructing a layering network index system by using the system elasticity index of the network information system.
The index analysis module is used for index correlation analysis and time evolution analysis, extracting key data and constructing a dynamic index network based on a network information system; and carrying out time evolution analysis on a system elastic index system of the network information system.
The analysis model construction module is used for calculating the weight of the index data and the weight of the system unit capacity. Based on the networking index system; establishing an element judgment matrix, and carrying out normalization processing on the element judgment matrix according to a characteristic value method to obtain a normalized weight matrix; and multiplying the normalized weight matrix by corresponding elements of the supermatrix to obtain a weighted supermatrix, and then carrying out ultimate power evolution processing based on the weighted supermatrix to form a stable supermatrix, wherein the values of each row of the supermatrix are the same at the moment, so that the weight corresponding to each unit is obtained.
The system capacity analysis module of the network information system is used for analyzing the system capacity of the network information system based on the OODA ring theory by utilizing the network index system and the system unit capacity weight to obtain an analysis result.
Claims (5)
1. A dynamic evolution system capacity analysis method based on simulation big data is characterized in that: the method comprises the following steps:
step (1) establishing a super network model of complex information system combat;
step (2) constructing a corresponding initial index set based on the super network model;
step (3) establishing a network information index system of the top level index;
step (4) establishing a system elasticity index system of the complex system;
step (5) constructing a dynamic index network, and carrying out time evolution analysis on the indexes;
step (6) of calculating complex system capacity based on time sequence,
the step (4) comprises the steps of aiming at the influence of network system elasticity and the self-characteristics of each subsystem unit on system elasticity along with time change, which are exhibited by the dynamic evolution of a complex system, synthesizing the characteristics of the complex system and the system countermeasure process, describing the complex system elasticity process through the system capacity dynamic change of the complex system, and constructing a system elasticity index system of the complex system;
the step (5) comprises the following steps:
(51) Determining index weight values corresponding to nodes of each layer of system elasticity of the complex system according to the super network model and the system elasticity index network of the complex system;
(52) The system elastic command decision capability is influenced by risk perception capability and communication capability, the system elastic recovery reconstruction capability is influenced by command decision capability and damage striking capability, and the evolution rules of the system in different stages are found based on the evolution of command decision capability nodes and damage striking capability nodes and the evolution of the continuous edge relationship, and the characteristic relationship of the evolution of different types of indexes along with time is analyzed;
(53) Carrying out correlation analysis on multiple indexes to find out the correlation among the indexes;
the step (6) comprises the following steps:
(61) To be used for
Time of day is for example->
Representing the sensing capacity of the system,/->
Representing the communication capability of the system,
Indicating the control capability of the system, +.>
Representing the striking power of the system;
(62) For a pair of
Time->
Carrying out importance ranking;
(63) The attack task, the attack intensity, the attack position and the hit probability index influence the effectiveness value of the hit capability, the index time influences each other, the attack task influences the attack intensity and the attack position, and the attack position is connected with the hit probability;
(64) Based on the indexes, setting a threshold value according to specific conditions by utilizing key index data extracted from an initial index set, extracting key indexes which have influence on system capacity and exceed the threshold value from a plurality of influence indexes, and carrying out normalization processing on the data by adopting an extremum method to obtain normalized data
，/>
Representing test data;
(65) The entropy weight method is adopted to calculate the weight of the processed index, and the result is expressed as:
(66) Based on the following calculation
Moment attack ability efficacy value->
：
Similar calculation
Other capability efficacy values at the moment;
(67) Dynamic evolution system
The system capacity at the moment is expressed as: />
wherein 
The weight values representing the sensing, communication, control and striking capabilities of the system are in the range of (0, 1), and the following conditions are satisfied:
(68) The comprehensive system capacity of the dynamic evolution of the whole stage is expressed as follows:
wherein ,
weight values representing different capacities in dynamic evolution process of system, and
。
2. the dynamic evolution system capability analysis method based on simulation big data according to claim 1, wherein the method comprises the following steps: the step (1) comprises the steps of constructing a four-layer super network model of a sensing network, a communication network, a command network and a striking network according to the structural relation of a complex system, wherein the super network model is expressed as:
wherein ,
representing a sensor network->
Representing a communication network,/->
Indicating command net +.>
Indicates a striking net->
Representing the connective relationship between nodes, +.>
Representation->
A sub-network;
each movable unit entity comprises 4 kinds of nodes, namely sensing nodes
Communication node->
Finger control node->
And hit node->
The interactive relationship between the active unit entities is a continuous edge, and the continuous edge relationship comprises a communication relationship
3. The dynamic evolution system capability analysis method based on simulation big data according to claim 1, wherein the method comprises the following steps: the step (2) comprises the following steps:
(21) Preprocessing basic data, including cleaning, reducing and integrating the data;
(22) The obtained initial index set comprises detection reconnaissance indexes, communication indexes, command control indexes and damage hit indexes.
4. The dynamic evolution system capability analysis method based on simulation big data according to claim 1, wherein the method comprises the following steps: step (3) comprises selecting typical indexes capable of reflecting Guan Jianneng force, and constructing a networked index system comprising indexes of elasticity, linkage, robustness, autonomy, flexibility and adaptability.
5. The system of the dynamic evolution system capability analysis method based on simulation big data according to any one of claims 1 to 4, wherein: the system corresponding to the analysis method comprises a simulation data management module, an index management module, a network information system index system construction module, an index analysis module, an analysis model construction module and a system capacity analysis module of a network information system;
the simulation data management module is used for carrying out data mining from mass simulation data and then carrying out data importing and storing;
the index management module is used for realizing the functions of adding, editing, deleting and inquiring indexes;
the network information system index system construction module is used for analyzing the characteristics of requirements and analysis objects and constructing a system elastic network index system based on a network information system;
the index analysis module is used for index correlation analysis and time evolution analysis, extracting key data and constructing a dynamic index network based on a network information system;
the analysis model construction module is used for calculating the weight of the index data and the weight of the system unit capacity;
the system capacity analysis module of the network information system is used for analyzing the system capacity of the network information system based on the OODA ring theory by utilizing the network index system and the system unit capacity weight to obtain an analysis result.
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