CN113067726B - Network node failure determination method based on double logic layer agents - Google Patents
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Abstract
The invention relates to the technical field of network failure node judgment, in particular to a network node failure judgment method based on double logic layers, which comprises the following steps: s1, constructing a failure type of a simulation structure of a double-logic-layer Agent; s2, selecting a key unit from the failure types to generate a failure event so as to simulate an intentional destruction event under the condition that a party is known to be concerned with the information unit partially through investigation, and obtaining a key service node failure event and a key network node failure event; or, the random selection unit generates a random number through the computer, and matches the random number with the unit mark to obtain a random service node point failure event and a random network node failure event. By constructing the failure type of the simulation structure of the double-logic-layer Agent and determining the failure nodes by selecting the failure events of the key units or randomly selecting the failure events of the units, all the failure nodes can be screened out to the maximum extent, and therefore robustness evaluation can be conveniently carried out on the practice of the failure nodes.
Description
Technical Field
The invention relates to the technical field of network failure node judgment, in particular to a network node failure judgment method based on double logic layer agents.
Background
Agent-based modeling and simulation mean that according to a complex adaptive system theory, countermeasure in the information era requires that system units and relationships can dynamically adapt to changes of internal and external environments, namely that a system structure has the characteristic of adaptive evolution. And secondly, the system structure is composed of system units and unit relations, each system is a typical Agent, and the system has the functions of sensing the running state of the system, interacting with other units and the like besides the functions of normal business processing and independent running, so that the system is very suitable for simulating the functions and behavior characteristics of the system units by establishing a system unit Agent model by utilizing the modeling idea of the Agent.
For example, a weapon deduction simulation structure is proposed in the document "war game deduction model research based on multi-agent system". The intelligent battlefield management system comprises a common war force intelligent agent (solider agent), a battle command intelligent agent (chieftagent) and a battlefield environment intelligent agent (battlefield agent). A simulation structure for combat simulation is proposed in the literature 'simulation architecture for combat simulation system of multiple agents'. The system comprises an information processing Agent, a platform Agent, a combat Agent, a comprehensive communication Agent and a command control Agent, and a single logic hierarchical structure is formed. Neither of the above two documents gives robustness to the simulation architecture, nor how to determine the validity of the network node.
Disclosure of Invention
The invention provides a network node failure judgment method based on double logic layer agents, which solves the technical problem that the simulation of the actual combat scene lacks of network node effectiveness judgment.
The invention provides a network node failure judgment method based on double logic layer agents for solving the technical problems, which comprises the following steps:
s1, constructing a failure type of a simulation structure of a double-logic-layer Agent;
s2, selecting a key unit from the failure types to generate a failure event so as to simulate an intentional destruction event under the condition that a party is known to be concerned with the information unit partially through investigation, and obtaining a key service node failure event and a key network node failure event;
or, the random selection unit generates a random number through the computer, and matches the random number with the unit mark to obtain a random service node point failure event and a random network node failure event.
Optionally, the failure type includes a service node failure type and a network node failure type, the service node failure type includes node failures in an information acquisition unit Agent, an information processing unit Agent, a decision control unit Agent and a terminal operational unit Agent, and the network node failure type includes node failures in an information network Agent model.
Optionally, the S2 specifically includes: and excavating key units of the system through node degree or betweenness parameters of a complex network theory.
Optionally, after S2, the method further includes: and collecting data of the network node failure event, wherein the data comprises the survivability, the system information guarantee capability change degree and the system command control capability change degree.
Optionally, the survivability includes: the number of times of successful backup succession, the total number of times of backup succession required, the time of system unit working abnormity, the time of successful completion of the succession of the backup unit, the number of times of successful resource borrowing, the number of times of total resource borrowing requirements, the time of insufficient resources and the time of successful resource borrowing.
Optionally, the system information guarantee capability variation degree includes a probe side coverage of the information obtaining Agent, a number of randomly generated targets, a number of target information obtaining units, an information processing capacity and an information processing time delay.
Optionally, the system command control capability change degree includes situation integrity, correctness, accuracy and timeliness, the number of times of the instruction control information received by the terminal combat unit, the sending time of the instruction control information, the correct receiving time, the total number of times of the information subscription request guarantee, the number of times of successful subscription, the sending time of the subscription information and the correct receiving time of the subscription information.
Optionally, after the failure event occurs, performing weighted calculation on the survivability, the system information guarantee capability change degree and the system command control capability change degree to obtain a performance reduction ratio, drawing a performance reduction curve, determining a system failure proportion tolerance according to the performance reduction tolerance, and further evaluating the robustness of the system.
Has the advantages that: the invention provides a network node failure judgment method based on a double logic layer Agent, which comprises the following steps: s1, constructing a failure type of a simulation structure of a double-logic-layer Agent; s2, selecting a key unit from the failure types to generate a failure event so as to simulate an intentional destruction event under the condition that a party is known to be concerned with the information unit partially through investigation, and obtaining a key service node failure event and a key network node failure event; or, the random selection unit generates a random number through the computer, and matches the random number with the unit mark to obtain a random service node point failure event and a random network node failure event. By constructing the failure type of the simulation structure of the double-logic-layer Agent and determining the failure nodes by selecting the failure events of the key units or randomly selecting the failure events of the units, all the failure nodes can be screened out to the maximum extent, and therefore robustness evaluation can be conveniently carried out on the practice of the failure nodes. The simulation precision of the system is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic flow chart of a network node failure determination method based on a dual logic layer Agent according to the present invention;
fig. 2 is a performance degradation curve diagram of the network node failure determination method based on the dual logic layer Agent.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention. The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
As shown in fig. 1, the present invention provides a network node failure determination method based on a dual logic layer Agent, including:
s1, constructing a failure type of a simulation structure of a double-logic-layer Agent;
s2, selecting a key unit in the failure type to generate a failure event so as to simulate an intentional destruction event under the condition that an opposite party (such as an enemy) knows that part of the opposite party cares about the information unit through investigation (such as reconnaissance), and obtaining a key service node failure event and a key network node failure event;
or, the random selection unit generates a random number through the computer, and matches the random number with the unit mark to obtain a random service node point failure event and a random network node failure event.
Firstly, a double-logic-layer Agent simulation structure is constructed. The agents form a simulation structure with double logic layers, including a service logic layer and a network logic layer. Wherein, the service logic layer simulates the essential intelligence, command and cooperative key service flow of three types of battles as shown above. The network logic layer is controlled by the information network Agent to generate a customizable information communication network, and maintains and establishes a mapping relation between the service logic layer and each node of the network logic layer.
The model units function as follows:
(1) The Agent-based modeling environment is mainly used for establishing a system unit Agent model to form a system unit Agent model library, so that the management and the multiplexing of the model are facilitated. In addition, other types of agents required by the system architecture simulation evaluation experiment, such as battlefield environment agents, can be established by utilizing the Agent-based modeling environment.
(2) The system unit Agent model base comprises four types of system unit type Agent models such as an information acquisition unit Agent, an information processing unit Agent, a decision control unit Agent, a terminal combat unit Agent and the like, and an information network Agent model, and different system structure models required by a simulation evaluation test can be formed by configuring parameters and relations of the four types of system unit type Agent models and the information network Agent model.
(3) The battlefield environment and force generation Agent is mainly used for simulating natural environments such as geography, weather, electromagnetism and the like, force deployment and force activities of both red and blue parties and the like, the battlefield environment and force generation Agent is used as external conditions and simulation excitation of system structure performance simulation model operation to simulate and generate various battlefield natural environment and force activity data, a corresponding counteraction scheme is formed through an information system and organized and implemented to form a simulation experiment closed loop of 'battlefield environment-perception-decision-control-action-battlefield environment', and the performance/counteraction performance and the like of a system structure are simulated and evaluated.
(4) The human-computer interaction module is responsible for tasks such as evaluating configuration, process control, state display and the like of an experiment, such as setting and modifying Agent parameters, controlling simulation operation (starting, suspending, resuming and terminating), displaying the operation state of the experiment and the like.
(5) And the analysis and evaluation module collects the operation data of the system unit Agent, the battlefield environment and the force generation Agent according to the system structure simulation evaluation algorithm and the data requirements, summarizes, analyzes and processes the operation data, and displays the analysis and evaluation effect in various modes such as forms, graphs and texts.
(6) The multi-Agent simulation operation support platform is a basis for developing simulation evaluation tests, and the various types of system unit agents only provide a group of basic services irrelevant to simulation application on the operation support platform, and mainly comprise time management services, agent member management services, information transmission services and the like. The time management service mainly ensures that the members of the multi-Agent carry out simulation time propulsion in a proper mode and sequence by providing uniformly understood global time and time propulsion algorithm and the like, and ensures the synchronization of the members in time, space and function; the member management service supports the operation of declaration, registration, revocation, quitting and the like of the Agent member; the information transfer service is mainly a communication information transfer format by means of standard bricks. Providing encapsulation, interpretation and message distribution of inter-Agent communications.
The simulation system mainly comprises four types of basic units, including an information acquisition unit, an information processing unit Agent, a decision control unit and a terminal combat unit. The following adopts a uniform Agent model structure to carry out simulation modeling on various system units, wherein the model basic structure consists of 3 modules of a sensor, a processor and an effector, wherein the sensor is used for sensing the change of the external environment and the self state of the model; the processor is used for processing the information obtained by the sensor according to a certain rule; the effector reacts to the perceived situation according to the processing result of the processor.
According to the double-logic-layer simulation structure, two types of failures are defined: and the service node fails (namely certain nodes of the information acquisition unit Agent, the information processing unit Agent, the decision control unit Agent and the terminal combat unit Agent4 are failed), and the network node fails (certain nodes in the information network Agent model are failed).
The service node failure is mainly found from the following 4 classes, specifically:
(1) Information acquisition unit Agent model
The information acquisition unit Agent model has the main functions of acquiring and preprocessing the objective state of a battlefield target through a sensor according to the type of the sensor, such as radar, ESM (electronic service management) and sonar, determining whether the target is found or not through a detection model of a processor, simulating the found target to generate a target condition, and outputting the target condition through the external information interaction relationship in the sensor. The main attribute parameters of the Agent model of the information acquisition unit comprise: unit deployment position, detection range, detection quality, situation output period and the like.
(2) Agent model of information processing unit
The Agent model of the information processing unit has the main functions that the sensor is used for judging the state of the Agent model, and under the normal condition, the receiving information acquisition unit sends target condition information, namely target condition, and the information is unpacked and subjected to format conversion and then sent to the processor; the processor carries out comprehensive/fusion processing according to the designed processing rule and fusion algorithm to generate complete, accurate and clear comprehensive conditions, and sends the comprehensive conditions to the effector; and the effector sends the comprehensive condition according to the interaction relation of the external information. The information processing unit Agent model attribute parameters comprise unit deployment position, input situation type/density, situation processing capacity, situation processing experiment, situation processing precision, situation output period and the like.
(3) Decision control unit Agent model
The decision control unit Agent model has the main functions of judging the self state by using a sensor and receiving the comprehensive condition sent by the information processing unit Agent under the normal condition; the processor performs situation judgment and planning according to a preset rule base and a database to generate a plan and a command; and the effector sends information such as a countermeasure plan and a command to the upper/lower level decision control unit or the terminal combat unit according to the relationship of the command control unit. The attribute parameters of the Agent model of the decision control unit comprise unit deployment position, task type, command level, decision period, upper and lower level relation, decision rule and the like.
(4) End combat unit Agent
The Agent model of the terminal combat unit has the main functions of judging the self state and receiving command commands by using a sensor; and performing striking judgment and striking effect evaluation by using a processor to form a striking result and sending the striking result to the Agent model of the battlefield environment. The attribute parameters of the end combat unit Agent model comprise unit positions, combat ranges, combat capability parameters (the number of configured weapons and ammunition, action indexes) and the like.
And the information network Agent model is used as a main component of an information system and provides a wide interconnection basic environment for four types of system units. The information network simulation adopts an Agent model to simulate the underlying network environment which various system units in the information system rely on during information interaction as a medium for information transmission among various units. The information network Agent model has the main functions of simulating the concurrent and multi-hop transmission process of information such as conditions, situations, instructions and the like in a wide area communication network and generating the effect of information transmission delay based on the factors such as the transmission capacity of the communication network, the information transmission quantity and the like. The information network Agent model comprises a sensor, a processor and an effector.
The information network Agent model is used for receiving the information sent by the system unit and storing the information in the local information table; determining an information sink unit according to the information interaction relation table; determining a communication path according to a communication network connection structure and a transmission protocol; and generating information waiting sending time and storing the information waiting sending time into an event table. The processor simulates the transmission of information in the communication network primarily by processing and generating communication time. The realization process is as follows: according to the communication network topology, communication capability and state, the events in the information transmission time table are processed, new waiting sending time (next hop is reached) and information transmission ending time (sink unit is reached) are generated, and the events are inserted into the event table. The processor continuously processes the events in the time table until no new event is generated, which means that the communication is finished and the information reaches the sink unit; the performance device sends information of the end of transmission to the sink unit.
Two methods were used: one is to select a critical unit to have a failure event, which is used to simulate the deliberate destruction of the information unit under the condition that the opposite party is known to be concerned with by our party through investigation. The calculation method utilizes parameters such as node degrees or betweenness of a complex network theory to dig out key units of the system; and secondly, randomly selecting a unit to generate a failure event, generating a random number through a computer, and determining the random number by matching with the unit mark. By the two methods, a key node can be calculated, or a failure node can be calculated by adopting a random number.
The two types and the two methods are matched to form 4 types of failure events such as key service node failure events, random service node failure events, key network node failure events, random network node failure events and the like.
In an optional scheme, the failure node is obtained by the method during/after random attack. And then collecting data of the network node failure event, wherein the data comprises the survivability, the system information guarantee capability change degree and the system command control capability change degree. The specific data acquisition comprises the following steps:
the backup unit take-over capability is mainly acquired: the number of times of successful backup succession, the total number of times of backup succession required, the time of system unit working abnormity, the time of successful completion of the succession of the backup unit, the number of times of successful resource borrowing, the number of times of total resource borrowing requirements, the time of insufficient resources, the time of successful resource borrowing and the like.
The information security capability change is mainly collected: the coverage of the probe side of the information acquisition Agent, the number of randomly generated targets, the number of information acquisition units of the targets, the information processing capacity and the information processing time delay.
The change of command and control capacity is mainly collected: situation integrity, correctness, accuracy and timeliness, the times of the instruction control information which is received by the terminal combat unit, the sending time of the instruction control information, the correct receiving time and the total times of the information subscription request guarantee; the successful times of subscription, the sending time of subscription information and the correct receiving time of subscription information.
As shown in fig. 2, according to an alternative scheme, after a failure event occurs, weighted calculation is performed on three items including the survivability, the system information guarantee capability change degree and the system command control capability change degree to obtain a performance reduction ratio, a performance reduction curve is drawn, the system failure proportion tolerance is determined according to the performance reduction tolerance, and then the system robustness is evaluated. I.e., a measure of the change in system performance after a failure event has occurred. The method mainly comprises the steps of carrying out weighted calculation on three items of backup unit succession, information guarantee capacity change and finger control capacity change to obtain a performance reduction ratio, drawing a performance reduction curve, determining a system failure proportion tolerance according to the performance reduction tolerance, and further evaluating the system robustness.
Has the advantages that: the invention provides a network node failure judgment method based on a double logic layer Agent, which comprises the following steps: s1, constructing a failure type of a simulation structure of a double-logic-layer Agent; s2, selecting a key unit from the failure types to generate a failure event so as to simulate an intentional destruction event under the condition that a party is known to be concerned with the information unit partially through investigation, and obtaining a key service node failure event and a key network node failure event; or, the random selection unit generates a random number through the computer, and matches the random number with the unit mark to obtain a random service node point failure event and a random network node failure event. By constructing the failure type of the simulation structure of the double-logic-layer Agent and determining the failure nodes by selecting the failure events of the key units or randomly selecting the failure events of the units, all the failure nodes can be screened out to the maximum extent, and therefore robustness evaluation can be conveniently carried out on the practice of the failure nodes. The simulation precision of the system is improved.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; the present invention may be readily implemented by those of ordinary skill in the art as illustrated in the accompanying drawings and described above; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (5)
1. A network node failure judgment method based on double logic layer agents is characterized by comprising the following steps:
s1, constructing a failure type of a simulation structure of a double-logic-layer Agent; the failure types comprise a service node failure type and a network node failure type, the service node failure type comprises node failures in an information acquisition unit Agent, an information processing unit Agent, a decision control unit Agent and a terminal combat unit Agent, and the network node failure type comprises node failures in an information network Agent model;
the information acquisition unit Agent is used for acquiring and preprocessing the objective state of a battlefield target through the sensor according to the type of the sensor, determining whether the target is found or not through a detection model of the processor, simulating the found target to generate a target condition, and outputting the target condition through an external information interaction relation in the sensor;
the information processing unit Agent is used for judging the state of the information processing unit Agent by using the sensor, receiving the target condition information sent by the information acquisition unit Agent, unpacking and converting the format of the target condition information, and sending the target condition information to the processor; the processor carries out comprehensive/fusion processing according to the designed processing rule and fusion algorithm to generate complete, accurate and clear comprehensive conditions, and sends the comprehensive conditions to the effector; the effector sends comprehensive conditions according to the interaction relation of external information;
the decision control unit Agent is used for judging the state of the decision control unit Agent by using the sensor and receiving the comprehensive condition sent by the information processing unit Agent; the processor performs situation judgment and planning according to a preset rule base and a database to generate a plan and a command; the effector sends the confrontation plan and the command to an upper/lower level decision control unit or a terminal combat unit Agent according to the relationship of the command control unit;
the terminal combat unit Agent is used for judging the state of the terminal combat unit by using the sensor and receiving a command; utilizing a processor to carry out striking judgment and striking effect evaluation, forming a striking result and sending the striking result to a battlefield environment Agent model; the information network Agent model is used as a main component of an information system and is used for providing an interconnection basic environment;
the information network Agent model is used for receiving the information sent by the system unit and storing the information in a local information table; determining an information sink unit according to the information interaction relation table; determining a communication path according to a communication network connection structure and a transmission protocol; generating information waiting sending time, storing the information waiting sending time into an event table, and simulating the transmission process of information in a communication network by a processor mainly through processing and generating communication time;
s2, selecting a key unit from the failure types to generate a failure event so as to simulate an intentional destruction event under the condition that a party is known to be concerned with the information unit partially through investigation, and obtaining a key service node failure event and a key network node failure event;
or, the random selection unit generates a failure event, generates a random number through a computer, and matches the random number with the unit mark to obtain a random service node failure event and a random network node failure event;
collecting data of a network node failure event, wherein the data comprises survivability, system information guarantee capability change degree and system command control capability change degree;
after a failure event occurs, carrying out weighted calculation on three items including survivability, system information guarantee capability change degree and system command control capability change degree to obtain a performance reduction ratio, drawing a performance reduction curve, determining system failure proportion tolerance according to the performance reduction tolerance, and further evaluating the robustness of the system.
2. The method for determining network node failure based on dual logic layer Agent according to claim 1, wherein the S2 specifically comprises: and excavating key units of the system through node degree or betweenness parameters of the complex network theory.
3. The dual logic layer Agent-based network node failure determination method of claim 1, wherein the survivability comprises: the number of times of successful backup succession, the total number of times of backup succession required, the time of system unit working abnormity, the time of successful completion of the succession of the backup unit, the number of times of successful resource borrowing, the number of times of total resource borrowing requirements, the time of insufficient resources and the time of successful resource borrowing.
4. The method for determining network node failure based on dual logic layer Agent according to claim 1, wherein the system intelligence guarantee capability variation degree comprises the probe side coverage of the intelligence acquisition Agent, the number of randomly generated targets, the number of objective intelligence acquisition units, the intelligence processing capacity and the intelligence processing delay.
5. The network node failure determination method based on the dual logic layer Agent according to claim 1, wherein the system command control capability change degree includes situation integrity, correctness, accuracy and timeliness, the times of command information received by the end combat unit, the sending time of the command information, the correct receiving time, the total times of information subscription request guarantee, the times of successful subscription, the sending time of the subscription information and the correct receiving time of the subscription information.
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