CN115619105A - Dynamic evolution system capability 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 system based on simulation big data, wherein the method comprises the following steps: establishing a super network model for complex information system battle; constructing a corresponding initial index set based on a hyper-network model; establishing a network information index system of a top-level index; establishing a system elastic index system of a complex system; constructing a dynamic index network, and carrying out time evolution analysis on indexes; calculating the complex system capacity based on the time series; 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 the basis of a dynamic evolution system capability analysis method, and the obtained result is more suitable for the characteristics of complex system activities, and is more reliable and more appropriate compared with the traditional method.
Description
Technical Field
The invention relates to the field of modeling and analyzing of a network information system, in particular to a dynamic evolution system capability 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 changed, and the future war is the war taking network and information as the center and is the embodiment of the universe combined operation; the war becomes more complex, and the confrontation characteristics among systems become increasingly prominent; how to describe the characteristics of complexity, dynamics, currency and the like of a system is a problem which needs to be solved urgently. The network information system is a complex nonlinear system, the structure and the relation of the system are complex and evolved, and the component systems are interdependent and related; the operational efficiency of the system is a key index for measuring the operational capacity of the system, how to realize the analysis of the system capacity of the network information system and how to provide a corresponding construction scheme according to the analysis result is a key problem to be solved in the network information system research.
The problems of high confidentiality level, complex source, difficult sharing and the like exist in the data in the field of real military, so that the persuasion of related research is greatly reduced. The simulation data generated by the simulation system deduction not only comprises the process and result data of system confrontation, but also comprises the operation instruction data of the commander participating in the battle and the like, and the data can well reflect the complexity rule of the battle. Therefore, it is very important to research the dynamic evolution system capability analysis method and system based on the simulation big data.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a dynamic evolution system capability 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:
(1) Establishing a super network model for complex information system battle;
(2) Constructing a corresponding initial index set based on a hyper-network model;
(3) Establishing a network information index system which changes instant excellent gathering and agility adaptation into a top index;
(4) Establishing a system elastic index system of a complex system;
(5) Constructing a dynamic index network, and carrying out time evolution analysis on indexes;
(6) Computing time series based complex architecture capability.
Further, the step (1) includes constructing a four-layer hyper-network model of a sensor network, a communication network, a command control network and a strike network according to the structural relationship of the complex system, wherein the hyper-network model is expressed as:
wherein ,
represents a sensor network,
Representing a communication network,
Network of presentation controls and
a net of percussion is shown,
representing the connection relationship between the nodes,
to represent
A subnet;
each active unit entity comprises 4 types of nodes which are respectively sensing nodes
Communication node
And control node
And striking node
The interaction relationship between the active unit entities is an edge connection relationship, which includes a communication relationship
Further, the step (2) comprises the steps of:
(21) Preprocessing basic data, including data cleaning, dimensionality reduction and integration;
(22) The initial index set obtained in the step (21) comprises a detection reconnaissance index, a communication index, a command control index and a striking damage index.
Further, the step (3) comprises selecting typical indexes capable of reflecting key capacity, and constructing a networked index system comprising elasticity, linkage, robustness, autonomy, flexibility and adaptability indexes.
Further, the step (4) includes, aiming at the network system elasticity emerging from the dynamic evolution of the complex system and the influence of the self-characteristics of each subsystem unit on the system elasticity along with the time change, integrating 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 the system elasticity index system of the complex system.
Further, the step (5) comprises the steps of:
(51) According to the hyper-network model and the system elasticity index network of the complex system, determining index weight values corresponding to each layer of nodes of the system elasticity of the complex system;
(52) The method comprises the steps that the command decision capability of system elasticity is influenced by risk perception capability and communication capability, the restoration reconstruction capability of the system elasticity is influenced by the command decision capability and damage striking capability, and the characteristic relation of different types of indexes evolving along with time is analyzed on the basis of the evolution of command decision capability nodes, damage striking capability nodes and the evolution of a connection-edge relation, so that the evolution rule of the system at different stages is found;
(53) And carrying out correlation analysis on the multiple types of indexes to find the correlation among the indexes.
Further, the step (6) comprises the steps of:
(61) To be provided with
The time of day is taken as an example,
the sensing capability of the system,
The communication capability of the presentation system,
The control capability of the representation system,
Representing the striking ability of the system;
(62) To pair
Time of day
、
、
And
the order of the importance is carried out,
the value range of the weight value of the sensing, communication, command and striking capability of the system is (0, 1);
(63) The attack task, the attack strength, the attack position and the hit probability index influence the effect value of the hit capability, the time of the indexes influence each other, the attack task influences the attack strength and the attack position, and the attack position is linked with the hit probability;
(64) Based on the indexes, the key index data extracted from the initial index set, namely, the threshold value is set according to specific conditions, the key index with the influence on the system capacity exceeding the threshold value is extracted from a plurality of influence indexes, and the data is normalized by adopting an extreme method to obtain normalized data
，
Represents experimental data;
(65) And carrying out weight calculation on the processed indexes by adopting an entropy weight method, wherein the result is expressed as:
(66) Calculated based on the following formula
Effective value of time attack capability
：
Computing by the same principle
Other capacity performance values at time;
(67) The dynamic evolution system is as follows
The system capability at time is expressed as:
wherein 
The weight value of the sensing, communication, finger control and percussion capability of the system is represented in a value range of (0, 1) and meets the requirements
；
(68) The comprehensive system capability of the whole stage dynamic evolution is expressed as:
wherein ,
represents weight values of different capabilities in the dynamic evolution process of the system, and
the dynamic evolution system capability 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 capability analysis module of a network information system;
the simulation data management module is used for mining data from mass simulation data and then importing and storing the data;
the index management module is used for realizing functions of adding, editing, deleting, inquiring and the like of indexes;
the network information system index system construction module is used for analyzing the characteristics of the demand and the analysis object 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 network information system dynamic index network;
the analysis model building module is used for calculating the weight of index data and the weight of system unit capacity;
and the system capacity analysis module of the network information system is used for analyzing the system capacity of the network information system based on an OODA ring theory by utilizing the network index system and the system unit capacity weight to obtain an analysis result.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: based on the sensing nodes, the communication nodes, the command control nodes and the attack nodes, a four-layer super-network model is established through a corresponding network, so that the overall appearance of the system can be clearly described; the method comprises the steps of establishing an index system which is changed into a top-level index by instant optimization and agility, constructing an elastic networked index system of a complex system, analyzing and constructing a dynamic index network according to the correlation of index time sequences, constructing an OODA loop, and adopting a weighted average method to realize the analysis of the system capability of dynamic evolution, and can provide assistance for the subsequent system contribution analysis, sensitivity analysis and system gravity center and system weak point search.
Drawings
FIG. 1 is a flow chart of a dynamic evolution system capability analysis method;
FIG. 2 is a schematic diagram of a hyper-network model of a complex architecture;
FIG. 3 is a schematic diagram of a system capability index system for a complex system;
FIG. 4 is a schematic diagram of a system elastic index system of a complex system;
FIG. 5 is a flow chart of dynamic indicator 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 frame diagram of a dynamic evolution system capability analysis system.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, the method for analyzing the capability of the dynamic evolution system based on the simulated big data comprises the following steps:
(1) Establishing a super network model for complex information system battle;
(2) Constructing a corresponding initial index set based on a hyper-network model;
(3) Establishing a network information index system which changes instant optimization and agility into top-level indexes;
(4) Establishing a system elastic index system of a complex system;
(5) Constructing a dynamic index network, and carrying out time evolution analysis on indexes;
(6) Computing complex architecture capabilities based on time series.
The step (1) of establishing the hyper-network model for complex information system combat specifically comprises the following steps: constructing a four-layer super-network model of a sensor network, a communication network, a command control network and a strike network according to the structural relationship of a complex system, wherein as shown in FIG. 2, the comprehensive system network is a network in the comprehensive network of the sensor network, the communication network, the command control network and the strike network, and each activity unit entity can be divided into 4 types of nodes which are respectively a sensor node S, a communication node C, a command control node D and a strike node A; the interaction relationship between the active unit entities is edge connection, which specifically includes: the communication relation represents the information transmission relation of the communication node to the communication (sensing, commanding and striking) node; the information sharing relation represents the information sharing relation among the sensing nodes; the information support relation indicates that the sensing node transmits the target information to the attacking node or the decision node; the command control relation indicates that the command control node issues command control information to the striking node, the subordinate command control node and the sensing node; the cooperative decision relationship represents the cooperative decision of finishing the same task through information sharing among the control nodes; the state reporting relation represents that the striking nodes (sensing nodes and finger control nodes) report the state information of the striking nodes to the superior finger control nodes; the scout relation indicates that the sensing node performs scout monitoring on the target and acquires related information of the target.
The formal description of the hyper-network model is:
； wherein ,
represents a sensor network,
Representing a communication network,
Indicating finger control net and
a net of percussion is shown,
, wherein 
Represents S, C, D, A subnet;
representing 4 types of nodes, and the specific description is shown in table 1;
and (3) representing the connection relation among the nodes, wherein the specific relation is shown in table 2.
Table 1: complex-architecture node types
Table 2: linking relationships between nodes of complex systems
The system capability analysis is to mine key indexes influencing the system capability from the mass data, firstly, to establish the relation between the mass data and the evaluation indexes, and the step (2) of establishing a corresponding initial index set based on the hyper-network model comprises the following steps: the method comprises the following steps of (1) including detection reconnaissance data, communication data, command control data and attack damage data as many as possible on the premise of having reliable data and meeting evaluation requirements; preprocessing basic data, including log data, instruction data and the like, mainly comprising data cleaning, dimension reduction and integration; the initial index set obtained in the above manner includes: detection reconnaissance indexes, communication indexes, command control indexes and attack damage indexes.
And (3) establishing a networked index system which is changed into top-level capability by instant optimization and agile adaptation, selecting typical indexes capable of reflecting key capability, and constructing the networked index system comprising the indexes such as elasticity, linkage, robustness, autonomy, flexibility, adaptability and the like around the top-level capability of the complex system due to the fact that related types of the influenced indexes are wide and the incidence relation among the indexes is complex, as shown in figure 3.
Establishing a system elasticity index system of a complex system, and aiming at the influence of the network system elasticity emerged by the dynamic evolution of the complex system and the self characteristics of each subsystem unit on the system elasticity along with the time change, integrating the characteristics of the complex system and the system confrontation process, describing the elasticity process of the complex system through the dynamic change of the system capacity of the complex system, wherein the complex system elasticity is the emerging result of the dynamic evolution of the network structure and is influenced by the self characteristics of the subsystem; therefore, the elastic process of the system is described by the change of the system capability of the complex system,
according to the characteristics of the complex system and the process analysis of system confrontation, the elastic process of the complex system is divided into 5 stages of information reconnaissance, situation judgment, command control, damage striking and recovery reconstruction.
As shown in fig. 4, an architecture flexible networked index architecture of a complex architecture is established,
because of numerous influence factors of the elasticity of a complex system, reliable test data is needed to be analyzed, and data processing is carried out on the basis of index data extracted by a hyper-network; for example, in the evolution process of the system, the command and decision capability of the system elasticity is influenced by the risk perception capability and the communication capability; the recovery and reconstruction capability of the system elasticity is influenced by the command decision capability and the damage and striking capability, so that the dynamic evolution of the whole system capability can be reflected laterally by analyzing the system elasticity process.
As shown in fig. 5, the detection scout data, the communication data, the command control data and the strike damage data are further preprocessed, wherein the data processing comprises log data, instruction data and the like, and after the data processing, a basic index system of the system capability is formed.
And (5) constructing a dynamic index network to perform time evolution analysis on the indexes, wherein the method specifically comprises the following steps:
carrying out correlation analysis among indexes by utilizing a maximum information interaction algorithm, and searching for correlation among different indexes; the relationship may be linear or nonlinear, in short, the association relationship between the indexes changes continuously along with the time evolution, so that the change rule between the indexes evolving along with the time can be obtained, and finally the dynamic index network evolving along with the time is obtained, and the specific algorithm comprises:
firstly, calculating mutual information and solving a maximum mutual information value;
dividing index pair
One kind of coordinate plane
Line of
Column grid
Probability density
Is the ratio of the number of sample points to the total number of samples in the index. Grid mesh
The mutual information value of (1) indicates the concentration degree of the index to the sample, and the correlation strength between the index pairs under the grid h division condition
Is composed of
Secondly, carrying out normalization processing on the maximum mutual information value;
assuming that the grid has multiple division methods
Thus defining the H mutual information characteristic value as
Finally, the maximum value of mutual information under different scales is selected as an MIC value which can be expressed as
Calculating the maximum information coefficient MIC as
Index pair
The maximum information coefficient is the maximum value in the characteristic value matrix, and the numerical value is closer to 1, which indicates that the association relationship between indexes is stronger;
according to the hyper-network model and the system elasticity index network of the complex system, determining index weight values corresponding to each layer of nodes of the system elasticity of the complex system;
analyzing the characteristic relation of different types of indexes evolving along with time according to the node evolution and the evolution of the connection edge relation, thereby discovering the evolution rules of the system at different stages; meanwhile, correlation analysis is carried out on the multiple types of indexes, and the correlation among the indexes can be found.
Step (6) the detailed steps of calculating the system capability of the complex system based on the time series are as follows:
the system activity is divided into several important phases: 4 stages of reconnaissance perception, information communication, command decision and attack and damage, as shown in fig. 6, an OODA loop is constructed based on a network, system capabilities of different stages are calculated by using a weighted average method, and the system capabilities of corresponding complex systems are calculated for different stages, specifically including:
the system capability of calculating the dynamic evolution at different stages is respectively as follows:
wherein 
，
The weight values representing the sensing, communication, command and percussion capabilities of different stage systems have the value range of (0, 1).
The comprehensive capability of calculating the whole stage complex system is expressed as:
wherein ,
weight values representing different activities in the integration stage of the whole system, an
In order to analyze the system capability of a dynamically evolved complex system, the following assumptions are proposed based on an 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 attack damage, and it is assumed that the system activity process is divided into four processes:
the phase assumes the perception capability at this time as
And is
The phase assumes that the communication capability at this time is
And is provided with
The phase assumes that the command decision capability at this time is
And is provided with
Phase hypothesis at this pointThe striking capability is
And is made of
This assumption shows that the system capability of the complex system is dynamically changed in different stages with time, which is consistent with the actual situation, and is assumed to be
In time, during the whole complex system activity process, the construction process of one loop,
based on the above assumptions, there is the following equation:
through the analysis of the whole process of the complex system activity oriented method, the method is respectively analyzed aiming at 4 stages of target perception, information communication, command decision and attack damage, 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 system capacity are obtained, but the relevance among indexes is not explained, for example, the command decision capacity is influenced by the interaction of the information communication capacity, so the mutual relation 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 system capability evaluation method of dynamic evolution, and the obtained evaluation result is more suitable for the characteristics of complex system activities, and is more reliable and more suitable compared with the traditional evaluation method.
As shown in fig. 7, the system for analyzing the capability of the dynamic evolution system based on the big simulation data of the present invention includes 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;
and the simulation data management module is used for mining data from the mass simulation data and then importing and storing the data. Classifying the simulation data, and on the premise of having reliable data and meeting analysis requirements, containing index data as much as possible, specifically including detection and investigation data, communication data, command and control data and attack and damage data; preprocessing the data, including log data, instruction data and the like, and mainly comprising data cleaning, dimension reduction and integration; the obtaining of the initial index data set in the above manner includes: detecting detection index data, communication index data, command control index data and striking damage index data; and finishing the import and storage of the index data.
And the index management module is used for realizing functions of adding, editing, deleting, inquiring and the like of indexes.
The network information system index system construction module is used for analyzing the characteristics of the demand and the analysis object and constructing a system elastic network index system based on the network information system. Aiming at the influence of the system elasticity of the network information system and the change of the self characteristics of each subsystem unit along with the time, which is brought forward by the dynamic evolution of the network information system, on the system elasticity. By integrating the characteristics of a network information system and the system countermeasure process, 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; acquiring flexible quantitative index data of a network information system, wherein the flexible quantitative index data comprises detection type data, communication type data, command control type data and attack damage type data; and constructing a hierarchical 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 network information system dynamic index network; and carrying out time evolution analysis on a system elastic index system of the network information system.
And the analysis model building module is used for calculating the weight of the index data and the weight of the system unit capacity. Based on the network index system; establishing an element judgment matrix, and carrying out normalization processing on the judgment matrix of the elements according to a characteristic value method to obtain a normalized weight matrix; and multiplying the normalized weight matrix by corresponding elements of the super matrix to obtain a weighted super matrix, and performing extreme power evolution processing based on the weighted super matrix to form a stable super matrix, wherein values of all rows of the super matrix are the same, and the weight corresponding to each unit is obtained.
And the system capacity analysis module of the network information system is used for analyzing the system capacity of the network information system based on an OODA ring theory by utilizing the network index system and the system unit capacity weight to obtain an analysis result.
Claims (8)
1. A dynamic evolution system capability analysis method based on simulation big data is characterized in that: the method comprises the following steps:
(1) Establishing a super network model for complex information system battle;
(2) Constructing a corresponding initial index set based on a hyper-network model;
(3) Establishing a network information index system of a top index;
(4) Establishing a system elastic index system of a complex system;
(5) Constructing a dynamic index network, and carrying out time evolution analysis on indexes;
(6) Computing time series based complex architecture capability.
2. The dynamic evolution system capability analysis method based on the simulated big data as claimed in claim 1, wherein: the step (1) comprises the steps of constructing a sensor network, a communication network, a command control network and a strike network four-layer super network model according to the structural relationship of a complex system, wherein the super network model is expressed as follows:
wherein ,
represents a sensor network,
Showing a communication network,
Indicating finger control net and
a net of percussion is shown,
representing the connection relationship between the nodes,
to represent
A subnet;
each active unit entity comprises 4 types of nodes which are respectively sensing nodes
Communication node
And control node
And striking node
The interaction relationship between the active unit entities is an edge connection relationship, which includes a communication relationship
3. The dynamic evolution system capability analysis method based on the simulated big data as claimed in claim 1, wherein: the step (2) comprises the following steps:
(21) Preprocessing basic data, including data cleaning, dimensionality reduction and integration;
(22) The initial index set obtained in the step (21) comprises a detection reconnaissance index, a communication index, a command control index and a striking damage index.
4. The dynamic evolution system capability analysis method based on the simulated big data as claimed in claim 1, characterized in that: and the step (3) comprises the steps of selecting typical indexes capable of reflecting key capacity, and constructing a networked index system comprising elasticity, linkage, robustness, autonomy, flexibility and adaptability indexes.
5. The dynamic evolution system capability analysis method based on the simulated big data as claimed in claim 1, wherein: and the step (4) comprises the steps of integrating the characteristics of the complex system and the countermeasure process of the system aiming at the network system elasticity emerged by the dynamic evolution of the complex system and the influence of the self characteristics of each subsystem unit on the system elasticity along with the time change, describing the elasticity process of the complex system through the dynamic change of the system capacity of the complex system, and constructing a system elasticity index system of the complex system.
6. The dynamic evolution system capability analysis method based on the simulated big data as claimed in claim 1, wherein: the step (5) comprises the following steps:
(51) According to the hyper-network model and the system elasticity index network of the complex system, determining index weight values corresponding to all layers of nodes of the system elasticity of the complex system;
(52) The method comprises the steps that the command decision capability of system elasticity is influenced by risk perception capability and communication capability, the recovery reconstruction capability of the system elasticity is influenced by the command decision capability and damage striking capability, and the characteristic relation of different types of indexes evolving along with time is analyzed on the basis of the evolution of command decision capability nodes and damage striking capability nodes and the evolution of a connection side relation system, so that the evolution rules of the system at different stages are discovered;
(53) And carrying out correlation analysis on the multiple types of indexes to discover the correlation among the indexes.
7. The dynamic evolution system capability analysis method based on the simulated big data as claimed in claim 1, characterized in that: the step (6) comprises the following steps:
(61) To be provided with
The time of day is taken as an example,
indicating the sensing capability of the system,
The communication capability of the presentation system,
The control capability of the system,
Representing the striking ability of the system;
(62) To pair
Time of day
、
、
And
the order of the importance is carried out,
the weighted value range of the sensing, communication, command and percussion capabilities of the system is (0, 1);
(63) The attack task, the attack strength, the attack position and the hit probability index influence the effect value of the hit capability, the time of the indexes influence each other, the attack task influences the attack strength and the attack position, and the attack position is linked with the hit probability;
(64) Based on the indexes, key index data extracted from the initial index set is utilizedSetting a threshold value according to specific conditions, extracting key indexes with influence on system capacity exceeding the threshold value from a plurality of influence indexes, and normalizing data by adopting an extreme method to obtain normalized data
，
Representing the test data;
(65) And carrying out weight calculation on the processed indexes by adopting an entropy weight method, wherein the result is expressed as:
(66) Based on the following formula
Effectiveness of moment attack capability
：
Computing by analogy
Other capacity performance values at time;
(67) The dynamic evolution system is
The system capability at time is expressed as:
wherein 
The weight value of the sensing, communication, finger control and percussion capability of the system is represented in a value range of (0, 1) and meets the requirements
；
(68) The comprehensive system capability of the whole stage dynamic evolution is expressed as:
wherein ,
represents weight values of different capabilities in the dynamic evolution process of the system, and
8. a dynamic evolution system capability analysis system based on simulation big data is characterized in that: the system capability analysis module 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 network information system capability analysis module;
the simulation data management module is used for mining data from massive simulation data and then importing and summing the data;
the index management module is used for realizing functions of adding, editing, deleting, inquiring and the like of indexes;
the network information system index system construction module is used for analyzing the characteristics of the demand and the analysis object 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 network information system dynamic index network;
the analysis model building module is used for calculating the weight of index data and the weight of system unit capacity;
and the system capacity analysis module of the network information system is used for analyzing the system capacity of the network information system based on an OODA ring theory by utilizing the network index system and the system unit capacity weight to obtain an analysis result.
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| CN111475899A (en) * | 2020-03-16 | 2020-07-31 | 北京航空航天大学 | Data link network reliability index system construction method based on hyper-network theory |
| CN113568782A (en) * | 2021-07-29 | 2021-10-29 | 中国人民解放军国防科技大学 | Dynamic recovery method for combat equipment system, electronic device and storage medium |
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