CN113836754A - Multi-agent simulation modeling oriented simulation method, device, equipment and medium - Google Patents
Multi-agent simulation modeling oriented simulation method, device, equipment and medium Download PDFInfo
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Abstract
The application relates to a simulation method, a device, equipment and a medium for multi-agent simulation modeling, wherein the method comprises the following steps: acquiring a multi-agent simulation task; splitting a multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes; constructing an intelligent agent model according to the simulation events; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing a simulation object, and the activity model is used for describing a simulation process; constructing a rule set model according to the entity model and the activity model; in the simulation process, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation. The method can realize complicated and changeable large-system modeling and simulation.
Description
Technical Field
The application relates to the technical field of system modeling and simulation, in particular to a simulation method, a device, equipment and a medium for multi-agent simulation modeling.
Background
The system simulation is a means for researching objective world, and forms a whole set of simulation system by carrying out abstract modeling on objective entities and researching the contents of entity composition, behavior logic, flow, rules, interaction relation and the like.
An Agent is an individual with independent environmental response capability, and a multi-Agent system is a set of multiple agents. A multi-Agent system is an open system, with agents both joining and leaving freely.
MABS is an abbreviation for Multi-Agent-Based-Simulation (Multi-Agent-Based-Simulation) in which multiple agents concurrently acquire environmental information and affect the environment, driving the Simulation to run.
However, in the MABS multi-agent simulation, the number of entities is large and is not fixed, the topological relation among the entities is dynamically developed and evolved, and the existing simulation method is difficult to support the complicated and changeable large-system simulation modeling; moreover, because the simulation step length is based on promotion, each intelligent agent needs to process self logic solution, the efficiency of multi-intelligent agent simulation is generally lower than that of discrete event simulation, and the multi-intelligent agent simulation needs to be processed by means of strong computing power, such as distributed computing, cloud computing technology and the like.
Disclosure of Invention
Therefore, it is necessary to provide a simulation method for multi-agent simulation modeling, which can realize complex and variable large-system modeling and simulation, aiming at the above technical problems.
The simulation method for multi-agent simulation modeling comprises the following steps:
acquiring a multi-agent simulation task; splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes;
constructing an intelligent agent model according to the simulation events; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing the simulation object, and the activity model is used for describing the simulation process;
constructing a rule set model according to the entity model and the activity model;
in the simulation process, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation.
In one embodiment, the agent model further comprises:
entity relationships between entity models, and activity relationships between activity models;
the entity relationship and the activity relationship each include: inheritance relationships, combination relationships, and priority relationships.
In one embodiment, the method further comprises the following steps:
determining scenario information of a plurality of simulation examples according to the multi-agent simulation task; the scenario information includes: entity attributes and number, simulation step length, simulation end time and rule, configuration parameters, activity parameters and simulation parameters;
constructing a simulation scenario model according to the scenario information; and the simulation scenario model issues the scenario information to the intelligent agent model.
In one embodiment, the method further comprises the following steps:
constructing a data structure model; the data structure model is used for describing the attributes of the intelligent agent model, the rule set model and the simulation scenario model and describing the interactive data structure among the intelligent agent model, the rule set model and the simulation scenario model;
the internal state interaction, the entity state interaction, the rule service and the default information are issued according to the data structure model.
In one embodiment, the agent model is constructed by the IOCM specification.
In one embodiment, the rule set model is implemented by a URule engine.
In one embodiment, the rule set model includes a rule set structure comprising:
an activity rule, a time advance rule, a failure determination rule, a success determination rule, and a selection rule.
MABS-oriented EAR modeling simulation device comprises:
the acquisition module is used for acquiring a multi-agent simulation task; splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes;
the intelligent agent model building module is used for building an intelligent agent model according to the simulation event; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing the simulation object, and the activity model is used for describing the simulation process;
the rule set model construction module is used for constructing a rule set model according to the entity model and the activity model;
and the simulation module is used for carrying out internal state interaction on the entity models and the activity models in the intelligent agent models, carrying out entity state interaction on the activity models among the intelligent agent models, and simultaneously carrying out rule service according to the rule set model to drive simulation operation.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a multi-agent simulation task; splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes;
constructing an intelligent agent model according to the simulation events; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing the simulation object, and the activity model is used for describing the simulation process;
constructing a rule set model according to the entity model and the activity model;
in the simulation process, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a multi-agent simulation task; splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes;
constructing an intelligent agent model according to the simulation events; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing the simulation object, and the activity model is used for describing the simulation process;
constructing a rule set model according to the entity model and the activity model;
in the simulation process, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation.
The multi-agent simulation modeling-oriented simulation method comprises the steps of firstly splitting a multi-agent simulation task into a plurality of simulation events, splitting the simulation events into simulation objects and simulation processes, respectively constructing an object-oriented entity model and a process-oriented activity model according to the simulation objects and the simulation processes, further integrating the entity models and the activity models, and constructing an agent model comprising the entity models and the activity models according to the simulation events; then, a rule set model is constructed according to the entity model and the activity model; on the basis, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation. Aiming at an MABS simulation system, the method carries out multi-stage splitting and hierarchical modeling on a complex multi-agent simulation task, and established activity models can carry out internal state interaction with entity models in real time and entity state interaction with other activity models in real time and simultaneously carry out rule service, so that the technical problems that the number of entities is large and the topological relation among the entities is dynamically developed and evolved in the MABS multi-agent simulation are solved, and the modeling and simulation of a complex and variable large system are realized; the method utilizes the graphical modeling thought to reduce the complexity and the time period of the intermediate process from modeling to operation, the modeling process is simple, rapid, efficient and visual, the modeling result is strong in practicability, lightweight, graphical and executable modeling and simulation are realized, and multi-scene simulation such as system simulation, system simulation and the like is supported.
Drawings
FIG. 1 is a flow diagram of a simulation method for multi-agent simulation modeling in one embodiment;
FIG. 2 is a block diagram of a simulation method for multi-agent simulation modeling in one embodiment;
FIG. 3 is a schematic diagram of a multi-agent simulation modeling oriented simulation method in one embodiment;
FIG. 4 is a diagram of the IOCM specification in one embodiment;
FIG. 5 is a diagram of a multi-agent simulation task in one embodiment;
FIG. 6 is a diagram of a multi-agent model in one embodiment;
FIG. 7 is a block diagram of a simulation apparatus for EAR modeling in one embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
As shown in fig. 1, the present application provides a simulation method oriented to multi-agent simulation modeling, which in one embodiment includes the following steps:
102, acquiring a multi-agent simulation task; the multi-agent simulation task is divided into a plurality of simulation events, and the simulation events are divided into simulation objects and simulation processes.
104, constructing an intelligent agent model according to the simulation event; the intelligent agent model comprises a solid model and an activity model; the solid model is used for describing a simulation object, and the active model is used for describing a simulation process.
And 106, constructing a rule set model according to the entity model and the activity model.
And 108, in the simulation process, carrying out internal state interaction on the entity models and the activity models in the intelligent agent models, carrying out entity state interaction on the activity models among the intelligent agent models, and simultaneously carrying out rule service according to the rule set model to drive the simulation operation.
An Agent is an individual with independent environmental response capability, has basic characteristics of autonomy, sociality, responsiveness, premonition and the like, can be regarded as a software program or an entity (such as a person, a vehicle, a robot and the like) embedded in a simulation environment, senses the environment through a sensor, autonomously acts on the environment through an effector and meets design requirements.
A multi-agent system is a collection of agents whose goal is to build large and complex systems into small, easily manageable systems that communicate and coordinate with each other. In a multi-Agent system, agents are autonomous, they may be different individuals or organizations, developed using different design methods and computer languages, and may be completely heterogeneous, without global data and without global control. This is an open system, with agents free to join and leave. Agents in the system cooperate together to coordinate their capabilities and goals to solve problems that cannot be solved by a single Agent.
MABS is a Multi-Agent-Based-Simulation in which multiple agents concurrently acquire environmental information and affect the environment, driving the Simulation to run. The time management mechanism of multi-agent simulation mostly adopts a discretized continuous time mechanism, the environmental state is continuously updated, and refined modeling can be realized, so that the real world is more accurately simulated.
As shown in fig. 2, the EAR, namely Entity-Activity-Rule, is a new modeling concept for modeling and simulating three elements, namely Entity-Activity-Rule, and is mainly applicable to the simulation of a complex large system, namely the system simulation.
Entity: the system can be independent and can separate specific individuals, and has objective existence, such as airplanes, vehicles, personnel and the like.
Moving: the process abstraction which is participated by single or a plurality of entities and lasts for a period of time has subjective abstraction, such as maneuver, flight, patrol, detection and the like.
Rule: the logic rules, behavior constraints, etc. required to be followed in the activity process are generally described in the form of a rule set, such as behavior rules (including maneuver rules, flight rules, etc.), interaction rules (including communication relationships, command relationships, etc.), constraint rules, etc.
A multi-agent simulation task can be divided into a plurality of simulation events, the simulation events can be divided into simulation objects and simulation processes according to the specific conditions of the events, the simulation objects and the simulation processes of each simulation event can be the same or crossed, and the same simulation object can participate in different simulation processes.
In the method, in the process of constructing an intelligent agent model, an object-oriented process and a process-oriented process are combined to describe the activity level and the attribute thereof together, and a static structure and a dynamic process are unified in a framework; not only supports graphical modeling language, but also can correspondingly produce structured natural language; providing a top-down design idea, analyzing a system model to a core layer by layer, wherein each layer can maintain a model core architecture and generate an incidence relation with a new model; the output can be UML, SysML and other system modeling languages.
UML: generally referred to as a unified modeling language. Unified Modeling Language (UML) is a standard Language for describing, visualizing, and documenting products for object-oriented systems, and is a non-proprietary third generation Modeling and specification Language. UML is an object-oriented design modeling tool, independent of any specific programming language.
SysML: the object management organization OMG determines to provide a new system Modeling language-SysML (systems Modeling language) as a standard Modeling language of system engineering on the basis of reusing and expanding the subset of UML 2.0. The purpose of SysML is to unify the modeling languages used in system engineering, as UML is used to unify the modeling languages used in software engineering.
As shown in table 1, comparing the structured method, the object-oriented method, and the object process method, it can be found that the method combines the object-oriented modeling and the process-oriented modeling, and can reasonably describe the modeled object by combining the advantages of the object-oriented modeling and the process-oriented modeling.
TABLE 1 comparison of the three methods
In this embodiment, the EAR modeling process is divided into two stages: intelligent agent modeling and rule set modeling.
In the agent modeling phase, an agent model is constructed. The intelligent body model refers to entity models and activity models which form the MABS simulation system, entity relations among the entity models and activity relations among the activity models; the entity relationship and the activity relationship each include: inheritance relationships, combination relationships, and priority relationships, for example: the passenger plane inherits to the airplane, and the airplane inherits to the aircraft; the detection activities consist of scanning activities, tracking activities, and recognition activities.
And in the rule set modeling stage, a rule set model is constructed. The rule set model drives rule set operations using a rule engine, and the rule set model includes a rule set structure and rule set logic. Wherein the ruleset architecture includes a plurality of simulation rules, such as: an activity rule, a time advancing rule, a failure judgment rule, a success judgment rule, a selection rule and the like; rule set logic refers to the mechanisms, flows and relationships between rules that the rules run, such as: the rule set detection start judgment rule takes precedence over the maneuver start judgment rule, and the like. The rule set model realizes the internal state transition of the entity model through the activity rule, and restricts the advancing step length, the advancing opportunity, the advancing mechanism and the like of time through the time advancing rule.
After the EAR modeling process is completed, the entity models and the activity models in the agent models carry out internal state interaction, the activity models between the agent models carry out entity state interaction, the activity models continuously obtain and change the entity states according to the internal state interaction and the entity state interaction, meanwhile, the rule service is carried out according to the rule set model, the simulation operation is driven, and the EAR simulation is carried out, wherein the rule service refers to logic judgment according to the simulation rules.
The EAR simulation is a system simulation. The system simulation is to establish a simulation model which can describe the system structure or behavior process and has a certain logical relationship or quantitative relationship on the basis of analyzing the properties and the mutual relationship of each element of the system according to the purpose of system analysis, and to perform test or quantitative analysis according to the simulation model to obtain various information required by correct decision.
The embodiment is also designed for MABS application scenarios, and time management based on time slices is added. The time management based on the time slices is to discretize the time according to a certain step length, carry out time driving, carry out situation perception and behavior output by the model according to the time slices of each step length, change the situation state, continue to advance the time, and circulate in this way. The activity model continuously acquires and changes the entity state, simultaneously performs rule service, and submits a time application, thereby driving simulation operation; wherein the time application is submitted according to a time advance rule.
The method carries out modeling and simulation design for a multi-Agent system, innovating a modeling design mode aiming at a large system with complex structure, uncertain composition, unfixed topological relation and variable logic relation, adopting a parallel Agent modeling framework aiming at the insufficient modeling means of the multi-Agent in the prior art, introducing an activity-oriented process element on the basis of object orientation, more meticulous and accurate description of the Agent, adding rule logic on the basis of three elements of a model, interaction and a protocol, and dynamically extending and contracting simulation scenes and scales; compared with system modeling methods such as Dodaf and Idef, the EAR is simpler and more convenient, is suitable for a scene of a lightweight building system simulation model, is convenient for later-stage upgrading maintenance, and realizes lightweight modeling; the method provides a novel graphical modeling idea, utilizes the characteristic of 'what you get is what you get' and carries out modeling through a good human-computer interaction interface by adopting block diagrams, connecting lines, buttons, text boxes and the like without learning and mastering programming languages, thereby improving the agility and the iteration speed of a modeling means and realizing convenient and visual graphical modeling; the method comprises conceptual modeling and implementation modeling, wherein the conceptual modeling is an overall architecture and a flow, the implementation modeling is code software development, the conceptual modeling and the implementation modeling are fused together, and modeling works of two stages are integrated into one stage, so that the method is a modeling mode which is richer and more landing than the conceptual modeling; the model established by the method is different from conceptual modeling, can be directly realized to the bottom logic details and is realized by a rule engine, namely a yule open source engine, and the rule engine is code-free modeling, so that the model established by the method can be directly driven by a simulation platform to run to form a simulation result, the modeling process is simplified, the modeling link is shortened, and the dynamic logic rule driving is realized by introducing the rule engine to realize executable modeling.
The multi-agent simulation modeling-oriented simulation method comprises the steps of firstly splitting a multi-agent simulation task into a plurality of simulation events, splitting the simulation events into simulation objects and simulation processes, respectively constructing an object-oriented entity model and a process-oriented activity model according to the simulation objects and the simulation processes, further integrating the entity models and the activity models, and constructing an agent model comprising the entity models and the activity models according to the simulation events; then, a rule set model is constructed according to the entity model and the activity model; on the basis, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation. Aiming at an MABS simulation system, the method carries out multi-stage splitting and hierarchical modeling on a complex multi-agent simulation task, and established activity models can carry out internal state interaction with entity models in real time and entity state interaction with other activity models in real time and simultaneously carry out rule service, so that the technical problems that the number of entities is large and the topological relation among the entities is dynamically developed and evolved in the MABS multi-agent simulation are solved, and the modeling and simulation of a complex and variable large system are realized; the method utilizes the graphical modeling thought to reduce the complexity and the time period of the intermediate process from modeling to operation, the modeling process is simple, rapid, efficient and visual, the modeling result is strong in practicability, lightweight, graphical and executable modeling and simulation are realized, and multi-scene simulation such as system simulation, system simulation and the like is supported.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, further comprising: determining scenario information of a plurality of simulation examples according to the multi-agent simulation task; the scenario information includes: entity attributes and number, simulation step length, simulation end time and rule, configuration parameters, activity parameters and simulation parameters; constructing a simulation scenario model according to scenario information; and the simulation tape-out model issues tape-out information to the intelligent agent model.
In this embodiment, the EAR modeling process further includes: the simulation envisages a modeling phase.
And in the simulation scenario modeling stage, a simulation scenario model is constructed. The simulation scenario model comprises a plurality of simulation examples, wherein the simulation examples are instantiation information of a simulation system and contain scenario information, namely: the attribute, participation type and quantity, simulation step length, simulation ending time or rule, initial configuration parameter, activity parameter and simulation parameter of the simulation model, and the like of the simulation entity. The simulation parameters include force parameters and operational parameters.
A simulation instance is the instantiated result of a simulation model, i.e., an abstraction of something concrete in the objective world, such as a plane parked at an airport. The simulation model is an object, such as an airplane, which forms an expression purpose for objectively describing a morphological structure through subjective awareness by means of physical or virtual representation. In the present embodiment, the simulation model refers to a simulation scenario model.
The simulation scenario model issues scenario information to the intelligent agent model, and the intelligent agent model updates the entity model and the structure model according to the scenario information. And continuously acquiring and changing the entity state by the updated activity model, and simultaneously performing rule service, driving simulation operation and performing EAR simulation.
In this embodiment, the complete modeling process is divided into three phases: intelligent modeling, rule set modeling and simulation scenario modeling. The EAR modeling has an operation mechanism based on state transition and dynamic instance generation, the simulation model establishment mainly comprises basic data and a basic scenario part, the simulation model instance is dynamically generated during operation, and scenario data is injected. The intelligent body modeling and the rule set modeling belong to a basic data part, and the simulation scenario modeling belongs to a basic scenario part.
The embodiment also designs an MABS application scene, and adds a factory mechanism and a dynamic combination mechanism.
The factory mechanism is a common type creation type design mode, the core spirit of the mode is a changed part in a packaging class, a personalized qualitative and qualitative part in the packaging class is extracted to be an independent class, and the purposes of decoupling, multiplexing and convenience for later maintenance and expansion are achieved by relying on injection. The core structure of the system has four roles, namely an abstract factory, a concrete factory, an abstract product and a concrete product.
In the embodiment, a factory mechanism is utilized to continuously generate new simulation examples, and a dynamic combination mechanism is utilized to combine a plurality of simulation examples and send the simulation examples to the intelligent agent model. The factory mechanism is an agent generation mechanism, and different agent factories are utilized to dynamically generate agents according to the requirements. The dynamic combination mechanism is a dynamic combination process in the generation process, the atomic model is used for generating the combination model, the interaction relation connection is dynamically carried out, and the rapid and flexible assembly is realized.
In one embodiment, further comprising: constructing a data structure model; the data structure model is used for describing the attributes of the intelligent agent model, the rule set model and the simulation scenario model and describing the interactive data structure among the intelligent agent model, the rule set model and the simulation scenario model; and internal state interaction, entity state interaction, rule service and proposal information sending are realized according to the data structure model.
In this embodiment, the EAR modeling process is divided into four phases: intelligent agent modeling, rule set modeling, simulation scenario modeling and data structure modeling, a schematic diagram is shown in fig. 3.
In the data structure modeling phase, a data structure model is constructed. The data structure model is metadata describing attributes of the models and performing interaction between the models and is composed of basic data types and composite data types. The objects described by the data structure include attributes, messages, events, states, configurations, etc., i.e., the data structure includes an attribute data structure, a message data structure, an event relationship data structure, a service relationship data structure, an entity state data structure, a configuration data structure, etc.
When the simulation runs, the simulation plan is issued to the entity Agent model and is used for updating a plurality of entity agents and injecting different parameter information, each entity Agent model consists of an entity state model and a plurality of arranged activity models, and the activity models acquire and change the states of the entity agents or other entities. The activity model calls the rule service of the rule set model to carry out logic judgment, and the simulation operation is driven by continuously changing the entity state and submitting the time application.
In one embodiment, the agent model is constructed by the IOCM specification.
As shown in fig. 4, an IOCM (Interface-Oriented-Composable-Modeling) specification, i.e., an Interface-Oriented Composable Modeling specification, is a new generation of parameterized, componentized Modeling specification. IOCM simulation model: the interface-oriented combinable simulation model has a well-defined simulation framework and interface description.
The IOCM is based on an object-oriented programming idea, adds a concept of 'combination is superior to inheritance', standardizes and standardizes the internal behavior of the model and an external interface, and provides a basic basis for interconnection and intercommunication interoperation between the models.
The internal behavior is an operational relationship which is executed by a system when the simulation object participates in a simulation event, and comprises a time transfer function, an event response function, an initialization function, a termination function and the like. The time transfer function may describe different responses to time between the simulation objects; the event response function can describe execution actions among all simulation objects, and can be attack, emission, detection, collection and the like; the initialization function can describe the initial relationship among the simulation objects; the termination function may describe termination relationships between simulation objects.
The external interfaces are ports of the simulation component, are used for connecting and communicating with other simulation components, and comprise configuration ports, data ports, status ports, service ports, drive ports and the like. The configuration port is used for modeling, the model reads the information of the configuration port to complete initialization work, and configuration can be transmitted to another model from one model to complete configuration distribution in the model; the data port is a data interaction port between the models, a sender sends the data actively, a receiver receives the data asynchronously, and a data response function is called back to process the data; the state port is a port for acquiring or setting the state of the model, and the data is read by adopting an active acquisition mode, namely pulling the data. The data publisher does not explicitly publish data, and when the data subscriber needs the state data, the data subscriber acquires the port data in a GET mode without binding a response event. The data updater can adopt a SET mode to update the state data; the service port is a remote method port between models, the initiator initiates service call, the server recalls the service response function, performs service and returns, and the initiator continues to operate after receiving the return. The platform provides system services, such as entity creation, link establishment, link disconnection, step length application, simulation entity list acquisition, port mapping list acquisition, new simulation branch development and the like; the driving port is a port for driving other or self main callback functions by the model, realizes serial, parallel and logic judgment branches of a workflow type, and can drive a core calculation function in the model to complete business work. Different interfaces can meet the model interaction function under different scenes and different applications.
In one embodiment, the rule set model is implemented by a URule engine.
The URule Pro is a pure Java rule engine which is independently developed and can strip business rules from business codes, and the stripped business rules are realized by using predefined semantic specifications; the rule engine evaluates the business rules and makes business decisions by accepting input data.
When the rule set model is established, the rule set can be input, tested and maintained by operating the web visual interface provided by the url.
In one particular embodiment, the multi-agent simulation task is: and modeling and simulating the security simulation system so as to explain the specific application of the EAR modeling and simulation method.
The application of the supportability simulation is to establish a simulation model capable of describing use and support activities in each stage of demonstration, development, production, deployment, use, support and the like of the equipment system, analyze, evaluate, optimize and verify the reasonability of each support element such as design characteristics, support resource allocation, support modes, repair levels and the like of the system by using experimental data obtained by executing a logic algorithm, and aims to improve the readiness integrity of the equipment system, shorten the development period and reduce the life cycle cost of the equipment.
As shown in the overall task architecture diagram of fig. 5, the support simulation system adopts the MABS simulation technology system, the intelligent agent modeling of which is shown in fig. 6, the rule set modeling of which is shown in table 2, and the data structure modeling of which is shown in tables 3 to 27. In the simulation scenario modeling, units and sites in the protective simulation scenario can be instantiated according to requirements, such as names, positions, relationships, process flows and the like.
TABLE 2 rule set modeling
Table 3 data structure modeling 1
Table 4 data structure modeling 2
Table 5 data structure modeling 3
Table 6 data structure modeling 4
TABLE 7 data Structure modeling 5
Table 8 data structure modeling 6
TABLE 9 data Structure modeling 7
TABLE 10 data Structure modeling 8
TABLE 11 data Structure modeling 9
TABLE 12 data Structure modeling 10
TABLE 13 data Structure modeling 11
TABLE 14 data Structure modeling 12
TABLE 15 data Structure modeling 13
TABLE 16 data Structure modeling 14
TABLE 17 data Structure modeling 15
Table 18 data structure modeling 16
TABLE 19 data Structure modeling 17
TABLE 20 data Structure modeling 18
Table 21 data Structure modeling 19
TABLE 22 data Structure modeling 20
TABLE 23 data Structure modeling 21
TABLE 24 data Structure modeling 22
TABLE 25 data Structure modeling 23
TABLE 26 data Structure modeling 24
TABLE 27 data Structure modeling 25
As shown in FIG. 7, in one embodiment, there is provided a multi-agent simulation modeling oriented simulation apparatus, comprising: an acquisition module 702, an agent model building module 704, a rule set model building module 706, and a simulation module 708. The obtaining module 702 is configured to: acquiring a multi-agent simulation task, splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into a simulation object and a simulation process; agent model building module 704 is configured to: according to the simulation events, an intelligent agent model is constructed, wherein the intelligent agent model comprises a solid model and an active model, the solid model is used for describing a simulation object, and the active model is used for describing a simulation process; the rule set model building module 706 is used to: constructing a rule set model according to the entity model and the activity model; the simulation module 708 is configured to: in the simulation process, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation.
In one embodiment, agent model building module 704 is further configured to: the agent model further comprises: entity relationships between entity models, and activity relationships between activity models; the entity relationship and the activity relationship each include: inheritance relationships, combination relationships, and priority relationships.
In one embodiment, the simulation planning model building module is further included for: determining scenario information of a plurality of simulation examples according to the multi-agent simulation task; the scenario information includes: entity attributes and number, simulation step length, simulation end time and rule, configuration parameters, activity parameters and simulation parameters; constructing a simulation scenario model according to scenario information; and the simulation tape-out model issues tape-out information to the intelligent agent model.
In one embodiment, the system further comprises a data structure model building module for: constructing a data structure model; the data structure model is used for describing the attributes of the intelligent agent model, the rule set model and the simulation scenario model and describing the interactive data structure among the intelligent agent model, the rule set model and the simulation scenario model; and internal state interaction, entity state interaction, rule service and proposal information sending are realized according to the data structure model.
In one embodiment, agent model building module 704 is further configured to: the agent model is constructed by the IOCM specification.
In one embodiment, the rule set model building module 706 is further configured to: the rule set model is implemented by a URule engine.
In one embodiment, the rule set model building module 706 is further configured to: the rule set model includes a rule set architecture that includes: an activity rule, a time advance rule, a failure determination rule, a success determination rule, and a selection rule.
For specific definition of the multi-agent simulation modeling-oriented simulation device, refer to the above definition of the multi-agent simulation modeling-oriented simulation method, and are not described herein again. The modules in the simulation device for multi-agent simulation modeling can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a simulation method oriented to multi-agent simulation modeling. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like. The computer equipment can be simulation equipment, the input device inputs related information into the simulation equipment, the processor executes programs in the memory to carry out combined simulation, and the display screen displays related simulation results.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. The simulation method for multi-agent simulation modeling is characterized by comprising the following steps:
acquiring a multi-agent simulation task; splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes;
constructing an intelligent agent model according to the simulation events; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing the simulation object, and the activity model is used for describing the simulation process;
constructing a rule set model according to the entity model and the activity model;
in the simulation process, the entity models and the activity models in the intelligent agent models carry out internal state interaction, the activity models between the intelligent agent models carry out entity state interaction, and meanwhile, the rule service is carried out according to the rule set model to drive the simulation operation.
2. The multi-agent simulation modeling-oriented simulation method of claim 1, wherein the agent model further comprises:
entity relationships between entity models, and activity relationships between activity models;
the entity relationship and the activity relationship each include: inheritance relationships, combination relationships, and priority relationships.
3. The multi-agent simulation modeling-oriented simulation method of claim 2, further comprising:
determining scenario information of a plurality of simulation examples according to the multi-agent simulation task; the scenario information includes: entity attributes and number, simulation step length, simulation end time and rule, configuration parameters, activity parameters and simulation parameters;
constructing a simulation scenario model according to the scenario information; and the simulation scenario model issues the scenario information to the intelligent agent model.
4. The multi-agent simulation modeling-oriented simulation method of claim 3, further comprising:
constructing a data structure model; the data structure model is used for describing the attributes of the intelligent agent model, the rule set model and the simulation scenario model and describing the interactive data structure among the intelligent agent model, the rule set model and the simulation scenario model;
the internal state interaction, the entity state interaction, the rule service and the default information are issued according to the data structure model.
5. The multi-agent simulation modeling-oriented simulation method according to any of claims 1 to 4, wherein the agent model is constructed by IOCM specification.
6. The multi-agent simulation modeling-oriented simulation method according to any of claims 1 to 4, wherein the rule set model is implemented by a URule engine.
7. The multi-agent simulation modeling-oriented simulation method according to any of claims 1 to 4, wherein the rule set model comprises a rule set architecture comprising:
an activity rule, a time advance rule, a failure determination rule, a success determination rule, and a selection rule.
8. Simulation device for multi-agent simulation modeling, comprising:
the acquisition module is used for acquiring a multi-agent simulation task; splitting the multi-agent simulation task into a plurality of simulation events, and splitting the simulation events into simulation objects and simulation processes;
the intelligent agent model building module is used for building an intelligent agent model according to the simulation event; the intelligent agent model comprises a solid model and an activity model; the entity model is used for describing the simulation object, and the activity model is used for describing the simulation process;
the rule set model construction module is used for constructing a rule set model according to the entity model and the activity model;
and the simulation module is used for carrying out internal state interaction on the entity models and the activity models in the intelligent agent models, carrying out entity state interaction on the activity models among the intelligent agent models, and simultaneously carrying out rule service according to the rule set model to drive simulation operation.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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