CN113435033B - Multi-resolution-based complex system operation flow simulation method and system - Google Patents
Multi-resolution-based complex system operation flow simulation method and system Download PDFInfo
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Abstract
The application relates to a multi-resolution-based complex system operation flow simulation method and system. The method comprises the following steps: the method comprises the steps of obtaining complex system tasks, dividing the complex system tasks into system layer tasks, grouping layer tasks and physical layer tasks, and modeling the system layer tasks according to a task planning diagram of the complex system tasks to obtain a task planning model; modeling the marshalling layer tasks by using a command state machine according to the marshalling task flow chart corresponding to each marshalling layer task to obtain a marshalling level flow model; modeling the physical layer task by using an action state machine according to a physical task flow chart of a physical corresponding to the physical layer task to obtain a physical level flow model; and simulating the complex system task according to the bottom layer simulation engine driving task planning model, the grouping level flow model and the entity level flow model. The method can be used for carrying out the process simulation of a complex system.
Description
Technical Field
The present application relates to the field of simulation technologies, and in particular, to a method and a system for simulating an operation flow of a complex system based on multiple resolutions.
Background
Due to the complexity of military missions, decision makers face significant challenges in analyzing the military complex architecture. The military complex system is an organic whole formed by a plurality of platforms from the aspect of composition; structurally, the system is nested and superposed with an early warning detection system, a communication system, a command decision system, a firepower striking system and an electronic countermeasure system. The military complex system has the following characteristics: the system has remarkable maneuverability and integration; the position of attack and defense cooperation information is prominent; the system networking characteristics are obvious; the system is resistant to dynamic variation. Therefore, from the perspective of a system operation flow, the operation flow of various entities in a complex system under the execution of different tasks is tried to be modeled so as to assist a decision maker in analyzing the operation mechanism of the complex system. The military complex system process modeling aims to standardize and express complicated system process structures and relationships in a task execution process, help military personnel understand and analyze the operation process of a complex system by recurring the actual occurrence steps of the task process, and further inspect and optimize the system process.
The purpose of the complex system-oriented flow modeling is to analyze whether the running flow relation of various tasks and entities in the complex system is reasonable, whether resource allocation is optimized and whether flow running is smooth, and further optimize the flow so as to improve the overall flow execution efficiency of the complex system. At present, the traditional process modeling methods are more, and mainly comprise a flow chart, an IDEF (inverse discrete edge flow) network, a Petri network, a UML (unified modeling language), a role activity diagram and the like. In recent years, a large amount of theoretical research and practical exploration are developed by scholars at home and abroad in the field of process modeling, the process modeling based on a Petri network, the process modeling method based on UML and the process modeling based on multi-Agent are mainly provided, and the method is widely applied in the research fields of battle command processes, equipment maintenance processes and the like.
The process modeling based on the Petri net combines the modeling technology based on the object with the Petri net technology, but the method has higher requirement on mathematical theory, has more complex modeling steps and is not suitable for the process modeling of a complex system. The method is applied to the process modeling in the military field, such as information processing and command control process modeling aiming at a strategic early warning command information system, command process modeling of an American multifunctional support power fire trip, operation process modeling of a missile weapon system, missile operation process modeling based on a hierarchical colored endowment Petri network, maintenance and guarantee process modeling of operation equipment and the like.
The UML-based process modeling method utilizes a static structure view, an example graph and a dynamic activity graph to acquire the problems of organization structure, activity, performance and the like of a system. The method is widely applied to the field of military modeling, and students study the modeling of cooperative air defense combat and anti-submarine combat processes of early warning machines on aircraft carriers, the modeling of air defense combat processes based on UML, the guarantee process models of military equipment and the like. In addition, UML and Petri networks are combined to model and analyze information flow of the ship-based combat system.
The method is based on multi-Agent process modeling, necessary parameters and behavior rules are given to a main body by simulating the adaptivity of the main body, and interaction occurs in the environment through the behavior criterion, parameter setting and learning process of the main body, so that the whole macroscopic phenomenon of the system is reflected. The students study a multi-Agent-based model for realizing the tactical command decision process modeling and the armored cooperative tactical command model.
The process modeling method mainly focuses on the process description function, so that the method cannot be suitable for process simulation of a complex system.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a multi-resolution-based method and system for simulating a complex system operation flow, which are capable of performing the complex system operation flow simulation.
A multi-resolution-based complex system operation flow simulation method comprises the following steps:
acquiring a complex system task, and dividing the complex system task into a system layer task, a grouping layer task and a physical layer task; the system layer task is a top layer task facing the complex system task, the grouping layer task is a task executed by grouping a plurality of entities, and the entity layer task is a task executed by a single entity;
modeling the system layer task according to the task planning diagram of the complex system task to obtain a task planning model;
modeling the grouping layer tasks by utilizing a command state machine according to a grouping task flow chart corresponding to each grouping layer task to obtain a grouping level flow model;
according to an entity task flow chart of an entity corresponding to the entity layer task, modeling the entity layer task by using an action state machine to obtain an entity-level flow model; the command state machine and the action state machine are both finite state machines;
and driving the task planning model, the grouping level flow model and the entity level flow model according to a bottom layer simulation engine, and simulating the complex system task.
In one embodiment, the method further comprises the following steps: and decomposing the complex system task to obtain a plurality of top-level tasks, constructing a task planning graph according to the execution sequence of the top-level tasks, and modeling the system-level tasks according to the task planning graph to obtain a task planning model.
In one embodiment, the method further comprises the following steps: grouping tasks and command entities corresponding to the grouping layer tasks are combined to generate a grouping process, and a grouping task flowchart is established according to the grouping process; setting flow parameters corresponding to the marshalling layer tasks, setting a command state machine according to the flow parameters, and modeling the marshalling layer tasks by using the command state machine and the marshalling task flow chart to obtain a marshalling level flow model.
In one embodiment, the method further comprises the following steps: constructing an action state machine corresponding to each entity according to the task action of each entity; and modeling the physical layer task by using an action state machine according to the physical task flow chart of the entity corresponding to the physical layer task to obtain a physical level flow model.
A multi-resolution based complex system workflow simulation system, the system comprising:
the system comprises a system flow database, a system flow modeling module, a system flow dynamic simulation module, a system flow analysis module and a system flow display module;
the system flow database is connected with the system flow modeling module, the system flow modeling module is respectively connected with the system flow dynamic simulation module and the system flow analysis module, and the system flow dynamic simulation module is used for executing the complex system operation flow simulation method based on the multi-resolution;
the system flow display module is used for displaying intermediate data of the system flow database, the system flow modeling module and the system flow analysis module.
In one embodiment, the system flow database comprises: and the engineering data submodule, the model data submodule and the analysis data submodule are used for managing and maintaining the model data, the engineering data and the analysis data of other modules.
In one embodiment, the system flow modeling module comprises: the system comprises a grouping view modeling submodule, a relation view modeling submodule, a deployment view modeling submodule, a task flow chart modeling submodule, a project management submodule and a primitive management submodule;
the system flow modeling module is used for providing a model data analysis basis for the system flow analysis module and the system flow dynamic simulation module through a grouping view, a relation view, a deployment view and a task flow diagram.
In one embodiment, the system flow analysis module analyzes vulnerability of the system, system operation evolution and system operation state monitoring by calculating model data derived by the system flow modeling module.
In one embodiment, the system flow display module comprises: the system comprises a project display submodule, a primitive display submodule, a model product display submodule and an analysis result display submodule; the system flow display module is used for interacting with a system user and providing project display, primitive display, model product display and analysis structure display.
According to the multi-resolution-based complex system operation flow simulation method and system, complex system tasks are divided into system layer tasks, grouping layer tasks and physical layer tasks; the system layer task is a top layer task oriented to the complex system task, the grouping layer task is a task executed by grouping a plurality of entities, and the entity layer task is a task executed by a single entity; modeling the system layer tasks according to a task planning diagram of the complex system tasks to obtain a task planning model; modeling the marshalling layer tasks by using a command state machine according to the marshalling task flow chart corresponding to each marshalling layer task to obtain a marshalling level flow model; according to an entity task flow chart of an entity corresponding to the entity layer task, modeling the entity layer task by using an action state machine to obtain an entity-level flow model; the command state machine and the action state machine are both finite state machines; and simulating the complex system task according to the bottom simulation engine driving task planning model, the grouping level flow model and the entity level flow model. In the embodiment of the invention, tasks are classified into three levels in simulation logic, each level respectively combs the execution logic of the task, a finite state machine is used for executing the task, a process model with various resolutions can be constructed according to different coarse granularities of a system process, and the process is modeled and simulated according to the business requirements of different levels.
Drawings
FIG. 1 is a schematic flow diagram illustrating a multi-resolution-based complex system operation flow simulation method according to an embodiment;
FIG. 2 is a block diagram of a multi-resolution based complex architecture workflow simulation system in one embodiment;
FIG. 3 is a diagram illustrating the correspondence between system flow modeling and SysML and DoDAF2.0 models, in accordance with an embodiment;
FIG. 4 is a flow diagram of modeling a system flow in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
In one embodiment, as shown in fig. 1, a method for simulating a multi-resolution-based complex system operation flow is provided, which includes the following steps:
and 102, acquiring a complex system task, and dividing the complex system task into a system layer task, a grouping layer task and a physical layer task.
The system layer task is a top layer task facing the complex system task, the grouping layer task is a task executed by grouping a plurality of entities, and the entity layer task is a task executed by a single entity.
The specific complex system task can be an automobile formation task, an unmanned aerial vehicle formation task, a ship formation task and the like.
And step 104, modeling the system layer tasks according to the task planning graph of the complex system tasks to obtain a task planning model.
After the complex system task is determined, the task planning diagram can be combed, so that modeling is carried out on the system layer task based on the task planning diagram, and simulation of the top layer task is facilitated.
And 106, modeling the marshalling layer tasks by using a command state machine according to the marshalling task flow chart corresponding to each marshalling layer task to obtain a marshalling level flow model.
The marshalling task flow chart can be determined after different marshalling layer tasks are determined, and then a simulation process can be automatically executed according to different states by utilizing a command state machine.
And 108, modeling the physical layer task by using the action state machine according to the physical task flow chart of the entity corresponding to the physical layer task to obtain a physical layer flow model.
The command state machine and the action state machine are both finite state machines.
A Finite State Machine (FSM) is a mathematical model that represents a Finite number of states and the behavior of transitions and actions between these states. Each state stores a series of information describing the model, and the system transitions from one state to another when certain events occur and certain conditions are met.
State refers to a condition of a model in a simulation process that must satisfy certain conditions, perform certain actions, or wait for certain events while the model is in a particular State. A state is composed of several actions. Whether the actions constituting the state are executed or not depends only on whether the last action is executed successfully or not, that is, the actions are executed one by one in sequence; actions (actions) refer to those atomic operations that can be performed in a state, being the smallest logical unit that can no longer be disassembled or prepared for further simulation. The atomic operation means that the atomic operation cannot be interrupted by other events in the running process and must be executed all the time; events (events) refer to those things that occupy a certain position in time and space and are meaningful to a state machine. Events typically cause state transitions that cause state machines to transition from one state to another; the activity required to be carried out during the Transition between the states is called Transition (Transition), the condition is bound with the Transition, and only a certain event occurs or meets a specific condition, the Transition from one state to another state can be realized; the conditions (conditions) may be grouped, with each Condition within the same group being an and relationship and each Condition between different groups being an or relationship.
And 110, simulating the complex system task according to the bottom simulation engine driving task planning model, the grouping level flow model and the entity level flow model.
In the multi-resolution-based complex system operation flow simulation method, a complex system task is divided into a system layer task, a grouping layer task and a physical layer task; the system layer task is a top layer task oriented to the complex system task, the grouping layer task is a task executed by grouping a plurality of entities, and the entity layer task is a task executed by a single entity; modeling the system layer tasks according to a task planning diagram of the complex system tasks to obtain a task planning model; modeling the marshalling layer tasks by utilizing a command state machine according to the marshalling task flow chart corresponding to each marshalling layer task to obtain a marshalling level flow model; modeling the entity layer task by using an action state machine according to an entity task flow chart of an entity corresponding to the entity layer task to obtain an entity level flow model; the command state machine and the action state machine are both finite state machines; and simulating the complex system task according to the bottom layer simulation engine driving task planning model, the grouping level flow model and the entity level flow model. In the embodiment of the invention, tasks are classified into three levels in simulation logic, each level respectively combs the execution logic of the task, and a finite state machine is used for executing the tasks, so that process models with various resolutions can be constructed according to different coarse granularities of system processes, and modeling simulation is carried out on the processes according to the business requirements of different levels.
In one embodiment, the complex system tasks are decomposed to obtain a plurality of top-level tasks, a task planning graph is constructed according to the execution sequence of the top-level tasks, and the system-level tasks are modeled according to the task planning graph to obtain a task planning model.
In one embodiment, a grouping process is generated by combination according to a grouping task and a command entity corresponding to a grouping layer task, and a grouping task flow chart is established according to the grouping process; and setting flow parameters corresponding to the marshalling layer tasks, setting a command state machine according to the flow parameters, and modeling the marshalling layer tasks by using the command state machine and the marshalling task flow chart to obtain a marshalling-level flow model.
In one embodiment, an action state machine corresponding to each entity is constructed according to task actions of each entity; and modeling the physical layer task by using an action state machine according to the physical task flow chart of the entity corresponding to the physical layer task to obtain a physical level flow model.
The complex system task decomposition process is given below by taking an aircraft carrier formation system as an example. The aircraft carrier formation system is an organic whole formed by a plurality of platforms such as an aircraft carrier, a carrier-based aircraft, a surface ship, a submarine and the like in terms of composition; structurally, the system is formed by nesting and overlaying a plurality of early warning detection systems, a communication system, a command decision system, a firepower striking system and an electronic countermeasure system. The characteristics of a formation combat system: the system has remarkable maneuverability and integration; the attack and defense cooperation information status is outstanding; the system networking characteristics are obvious; the system is resistant to dynamic changes. Typical combat styles of aircraft carrier formation can be classified into air combat, army combat, warship combat, submarine combat, and the like. Different system layer tasks, grouping layer tasks and physical layer tasks can be further subdivided according to specific styles.
The top-level battle tasks can be divided into air defense battles, land battles, warship battles, submarine battles and the like. On the basis of the top-level task, the decomposition can be further refined according to the specific task execution stage. And finally generating a task planning model facing the complex system in the task layer modeling stage.
The grouping level tasks may be classified as maneuver tasks, area patrols, airline patrols, electronic interference, and the like.
Physical layer tasks can be divided into starting maneuvers, stopping maneuvers, hitting targets, etc.
And aiming at the grouping layer task and the physical layer task in the process modeling frame, respectively using a command state machine and an action state machine to perform modeling. The command state machine is constructed aiming at the marshalling task of the command entity, and the action state machine is constructed aiming at the tactical task of the combat entity, namely a specific weapon platform. The combat process of the entity is modeled by the state machine, and the state machine is only related to the combat task at the entity level when being constructed, and specific task execution entities are not bound. Actions within a particular state machine are also not tied to a particular entity.
In the specific editing process of the state machine, states are represented as nodes, transitions between the states are represented as links between the nodes, transition conditions are defined on the link relations, multiple groups or multiple transition conditions can exist on the same connection, the transition conditions in the same group are in an AND relation, and the conditions in each group are in an OR relation.
The entities include action entities, command entities, and weapon entities. The action entity is a leaf node in the system operation flow chart or the network; the command entities are non-leaf nodes and represent a grouping of action entities; the weapon entity represents a weapon mounted by the action entity.
In one embodiment, as shown in fig. 2, a multi-resolution-based complex system operation flow simulation system is provided, which includes:
the system comprises a system flow database, a system flow modeling module, a system flow dynamic simulation module, a system flow analysis module and a system flow display module;
the system flow database is connected with the system flow modeling module, the system flow modeling module is respectively connected with the system flow dynamic simulation module and the system flow analysis module, and the system flow dynamic simulation module is used for executing the complex system operation flow simulation method based on the multi-resolution; the system flow display module is used for displaying intermediate data of the system flow database, the system flow modeling module and the system flow analysis module.
In one embodiment, the system flow database comprises: and the engineering data submodule, the model data submodule and the analysis data submodule are used for managing and maintaining the model data, the engineering data and the analysis data of other modules.
In one embodiment, the system flow modeling module comprises: the system comprises a grouping view modeling submodule, a relation view modeling submodule, a deployment view modeling submodule, a task flow chart modeling submodule, a project management submodule and a primitive management submodule; the system flow modeling module is used for providing a model data analysis basis for the system flow analysis module and the system flow dynamic simulation module through a grouping view, a relation view, a deployment view and a task flow diagram.
In one embodiment, the system flow analysis module analyzes vulnerability of the system, system operation evolution and system operation state monitoring by calculating model data derived by the system flow modeling module.
In one embodiment, the system flow display module comprises: the system comprises a project display submodule, a primitive display submodule, a model product display submodule and an analysis result display submodule; the system flow display module is used for interacting with a system user and providing project display, primitive display, model product display and analysis structure display.
Specifically, in order to ensure the consistency and logic rationality of system process data, a SysML modeling language is taken as a basis, IDEF, UML and other modeling languages are combined, a DoDAF Framework and a Unified Architecture Framework (UAF) are taken as guiding ideas, modeling specifications such as IDEFX, organizational structure diagrams and the like of multiple views and different abstraction levels are fully utilized, and a complex system is analyzed, described, designed and checked in the forms of a grouping view, a relation view, a task flow diagram, a deployment view and the like.
The correspondence between the 4 views in the complex system flow modeling and the dodaf2.0 and SysML is shown in fig. 3.
The grouped view extends OV4 of DoDAF and the module definition diagram of SysML, describing the organizational structure of the architecture.
The relational graph extends the internal module graphs of OV-2, OV-3 and SysML of DoDAF, and describes the relations among organization units, including communication relations, detection relations, battle relations, command relations, reactance relations, support relations, bearing relations, logistics support relations, defense relations, attack relations and the like.
The deployment view expands the parameter maps of OV-1 and SysML of DoDAF, and simultaneously utilizes a GIS map to graphically describe the fighting concept and describe the geographic position of an entity in the fighting process in detail.
The task flow chart extends the OV-5 of DoDAF and the activity diagram of SysML, describes dynamic changes in the battle process, and provides a data source for system simulation and system evaluation.
The modeling of the system flow products based on the DoDAF and the SysML must follow a certain development sequence, firstly a grouping view is established, a relation diagram and a deployment view are established on the basis, then a task flow diagram is established, and finally the established model is subjected to dynamic simulation verification and evaluation analysis to ensure the correctness and the normalization of the model. The main steps of the system flow modeling are shown in fig. 4.
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 can include non-volatile and/or volatile memory. 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 (Rambus) direct RAM (RDRAM), direct memory 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, and these are all 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 (9)
1. A multi-resolution-based complex system operation flow simulation method is characterized by comprising the following steps:
acquiring a complex system task, and dividing the complex system task into a system layer task, a grouping layer task and a physical layer task; the system layer task is a top layer task facing the complex system task, the grouping layer task is a task executed by grouping a plurality of entities, and the entity layer task is a task executed by a single entity;
modeling the system layer task according to the task planning diagram of the complex system task to obtain a task planning model;
modeling the grouping layer tasks by utilizing a command state machine according to a grouping task flow chart corresponding to each grouping layer task to obtain a grouping level flow model;
according to an entity task flow chart of an entity corresponding to the entity layer task, modeling the entity layer task by using an action state machine to obtain an entity-level flow model; the command state machine and the action state machine are both finite state machines;
and driving the task planning model, the grouping level flow model and the entity level flow model according to a bottom layer simulation engine, and simulating the complex system task.
2. The method according to claim 1, wherein modeling the hierarchy-level tasks according to a mission planning graph of the complex hierarchy tasks to obtain a mission planning model comprises:
and decomposing the complex system task to obtain a plurality of top-level tasks, constructing a task planning graph according to the execution sequence of the top-level tasks, and modeling the system-level tasks according to the task planning graph to obtain a task planning model.
3. The method according to claim 1, wherein the modeling of the grouping layer task by using a command state machine according to the grouping task flow chart corresponding to each grouping layer task to obtain a grouping level flow model comprises:
generating a grouping process in a combined mode according to the grouping tasks and the command entities corresponding to the grouping layer tasks, and establishing a grouping task process diagram according to the grouping process;
setting flow parameters corresponding to the marshalling layer tasks, setting a command state machine according to the flow parameters, and modeling the marshalling layer tasks by using the command state machine and the marshalling task flow chart to obtain a marshalling level flow model.
4. The method of claim 1, wherein the modeling the physical layer task using an action state machine according to a physical task flow graph of a physical layer task corresponding to the physical layer task to obtain a physical layer flow model comprises:
constructing an action state machine corresponding to each entity according to the task action of each entity;
and modeling the physical layer task by using an action state machine according to the physical task flow chart of the entity corresponding to the physical layer task to obtain a physical level flow model.
5. A multi-resolution-based complex system workflow simulation system, the system comprising:
the system comprises a system flow database, a system flow modeling module, a system flow dynamic simulation module, a system flow analysis module and a system flow display module;
the system flow database is connected with the system flow modeling module, the system flow modeling module is respectively connected with the system flow dynamic simulation module and the system flow analysis module, and the system flow dynamic simulation module is used for executing the complex system operation flow simulation method based on multi-resolution according to any one of claims 1 to 4;
the system flow display module is used for displaying intermediate data of the system flow database, the system flow modeling module and the system flow analysis module.
6. The system of claim 5, wherein the hierarchy flow database comprises: and the engineering data submodule, the model data submodule and the analysis data submodule are used for managing and maintaining the model data, the engineering data and the analysis data of other modules.
7. The system of claim 5, wherein the system flow modeling module comprises: the system comprises a grouping view modeling submodule, a relation view modeling submodule, a deployment view modeling submodule, a task flow chart modeling submodule, a project management submodule and a primitive management submodule;
the system flow modeling module is used for providing a model data analysis basis for the system flow analysis module and the system flow dynamic simulation module through a grouping view, a relation view, a deployment view and a task flow diagram.
8. The system of claim 5, wherein the system flow analysis module analyzes vulnerability of the system, system operation evolution, and system operation state monitoring by calculating model data derived by the system flow modeling module.
9. The system of claim 5, wherein the architecture flow display module comprises: the system comprises an item display submodule, a primitive display submodule, a model product display submodule and an analysis result display submodule;
the system flow display module is used for interacting with a system user and providing project display, primitive display, model product display and analysis structure display.
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