KR20190061616A - Method and apparatus for simulation model optimization using component-based dynamic model reconstruction - Google Patents

Method and apparatus for simulation model optimization using component-based dynamic model reconstruction Download PDF

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KR20190061616A
KR20190061616A KR1020170160180A KR20170160180A KR20190061616A KR 20190061616 A KR20190061616 A KR 20190061616A KR 1020170160180 A KR1020170160180 A KR 1020170160180A KR 20170160180 A KR20170160180 A KR 20170160180A KR 20190061616 A KR20190061616 A KR 20190061616A
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simulation model
optimization
component
combination
model
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KR1020170160180A
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Korean (ko)
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배장원
이천희
강동오
김기호
백의현
정준영
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한국전자통신연구원
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Abstract

Provided are a simulation model optimization method using component based dynamic model reconfiguration and an apparatus thereof. An optimization target is established, a simulation model preset at an initial stage is executed, and the simulation model execution result is compared with the optimization target. If the simulation model execution result does not satisfy the optimization target, a component combination proper for the simulation module is searched and a parameter combination proper for the simulation model is found. The simulation model is reconfigured based on the searched component combination and the found parameter combination and the reconfigured simulation model is executed. Based on a comparison between the execution result of the reconfigured simulation model and the optimization target, an optimal simulation model is acquired. Simulation execution time may be shortened by exchanging dynamic components.

Description

[0001] The present invention relates to a method and apparatus for optimizing a simulation model using a component-based dynamic model reconstruction,

The present invention relates to simulations, and more particularly, to a method and apparatus for optimizing a simulation model utilizing component-based dynamic model reconstruction.

Modeling and simulation are widely used to understand, analyze, and design complex systems in many areas. This is due to the development of modeling and simulation methods. In other words, it is changing from past ad hoc modeling and simulation methods to systematic development based on system theory. The development of these modeling and simulation technologies is based on the idea that a model is developed at a low cost by clearly distinguishing between a simulation model and an engine and a complex model is expressed as a combination of modular components, Increase understanding and analysis, and increase opportunities to reuse existing models during development. This reduces the time and cost of the overall modeling and simulation technology and provides a theoretical and practical tool for efficiently analyzing complex systems that can not be analyzed by hermetic methods. An example of such a technology is the Discrete Event System Specification (DEVS) formalism, which is currently being used to develop simulation models in various fields both at home and abroad.

On the other hand, the core of modeling and simulation technology based on system theory is a method of modular component development that can express the dynamics of the whole system through the separation of model and simulation engine and the combination of components constituting the system. The separation of the model and the simulation engine and the combination of the components that make up the system enable the development of the simulation framework and provide an environment in which various components can be freely configured within the framework. Model Dynamic reconfiguration refers to changing the combination of components that make up the model during simulation execution. In other words, the number of components or the connection method of the components can be changed during the simulation, which is based on the function of managing or modifying information of a definite model component. Therefore, model dynamic reconfiguration can be supported in an environment with a theoretical basis of modeling and simulation, such as formalism.

Model optimization represents the task of finding the optimal combination of parameters of a model in the direction of increasing the performance of the model. In this process, various methods are used to analyze the performance of the model. Simulation-based optimization analyzes the performance of a simulation model as a simulation result and shows how to find a combination of parameters of the model for good performance. Especially, it is difficult to apply the general analytical method to the analysis of a model having complex and nonlinear characteristics. However, it is advantageous to solve this problem by simulation analysis. In recent years, along with the development of machine learning and artificial intelligence technologies, simulation-based optimization has also been developed in various ways.

However, the existing simulation-based optimization is centered on the model parameters, and there is a disadvantage that the optimization of the model beyond the range that the parameter can not express can not be achieved.

A problem to be solved by the present invention is to provide a method and apparatus for achieving model optimization using model parameters and dynamic model reconstruction.

A method according to an embodiment of the present invention is a method for a simulation model optimization, the method comprising: setting an optimization target; The apparatus comprising: initially executing a predetermined simulation model; The apparatus comprising: comparing the result of the simulation model execution with the optimization target; Searching for a combination of components suitable for the simulation model if the result of the execution of the simulation model does not satisfy the optimization target and searching for a combination of parameters suitable for the simulation model; Reconstructing the simulation model based on the searched component combination and parameter combination; And executing the reconstructed simulation model, wherein an optimal simulation model is obtained based on a comparison between the result of executing the reconstructed simulation model and the optimization target.

Determining a combination of the parameters until a result of the execution of the simulation model satisfies the optimization target, reconstructing the simulation model, executing the reconstructed simulation model, and comparing the optimization goals Can be repeatedly performed.

The reconstructing of the simulation model may perform a dynamic exchange of components of the simulation model based on the searched component combination to reconstruct the simulation model.

The step of finding the combination of parameters may comprise the step of identifying, from a database comprising a module having a module having an independent input / output interface, a component to be utilized by clustering the component on the basis of input and output interfaces have.

The finding of the combination of parameters may comprise generating a new component for achieving the optimization goal using the optimization goal, existing optimization result data, and the components that make up the simulation model.

Wherein reconfiguring the simulation model further comprises reconfiguring the simulation model using the newly generated component, wherein the optimization result data includes optimization history including component and parameter information for a simulation model that achieves a random optimization goal Information.

According to another aspect of the present invention, there is provided an apparatus for optimizing a simulation model, comprising: an input / output unit configured to receive data; And a processor coupled to the input / output unit and configured to perform a simulation model optimization, wherein the processor is configured to perform the optimization of the simulation model when the result of executing the simulation model that is initially set in advance does not satisfy the preset optimization target Searching for a combination of components suitable for the simulation model, and reconstructing and executing the simulation model based on the searched component combination and parameter combination, and comparing the result of executing the reconstructed simulation model and the optimization An optimal simulation model is obtained based on the comparison of the targets.

Wherein the processor is further configured to find a combination of the component and parameter suitable for the simulation model and reconstruct and execute the simulation model until the result of the execution of the simulation model satisfies the optimization target, And comparing the targets to obtain the optimal simulation model.

The processor may be further configured to perform a dynamic exchange of components of the simulation model based on the searched component combination to reconstruct the simulation model.

The apparatus may further include a component database storing components composed of modules having independent input / output interfaces. The processor may be configured to cluster components based on input and output interfaces to identify components suitable for the simulation model from the component database.

The apparatus may further include an optimization history database storing optimization result data, which is optimization history information including component and parameter information for a simulation model that achieves a certain optimization target. At this time, the processor generates a new component for achieving the optimization target by using the optimization target, the optimization result data, and the components that configure the simulation model, Lt; / RTI >

The processor comprising: a model optimization estimator configured to execute the simulation model and to perform a model optimization evaluation by comparing the execution result and the optimization target; An optimal combination navigator configured to obtain optimal combination of components and parameters to achieve the optimization goal; A dynamic model combiner for reconstructing the simulation model based on the combination of components and parameters; And an optimal component generator configured to newly generate a component required to achieve the optimization target.

According to the embodiment of the present invention, by applying the model parameters and the dynamic model reconstruction method, a wider range of model optimization is performed and a new component is presented using the existing optimization result, Can be achieved.

In particular, according to embodiments of the present invention, simulation-based model optimization can be improved in two directions.

First, a variety of simulation-based model optimization methods are provided. In the conventional method, we tried to optimize the model by searching for a combination of various parameters of the simulation model. However, a case where the parameter combination does not achieve a certain level or more of optimization is uttered. Therefore, in the embodiment of the present invention, the search range of the parameter and the component model is searched to consider the higher dimensional search range, and in particular, the simulation execution time is shortened through the dynamic component exchange.

Second, when the combination of the parameter and the existing component model is insufficient, a function of presenting a new component in consideration of the existing optimization history is provided. Thus, it is expected that optimization of various simulation models can be achieved more efficiently.

1 is a structural diagram of a simulation model optimizing apparatus according to an embodiment of the present invention.
2 is a flowchart of a simulation model optimization method according to an embodiment of the present invention.
3 is an operation diagram illustrating that an optimal model acquisition process and an optimal component process are performed in the simulation model optimizing apparatus according to the embodiment of the present invention.
FIG. 4 is a diagram illustrating a process of searching and confirming a component in a DB that can be utilized in model reconstruction in an optimal model combiner according to an embodiment of the present invention.
5 is a diagram illustrating component registration and addition in accordance with an embodiment of the present invention.
6 is a structural diagram of a simulation model optimizing apparatus according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when an element is referred to as " comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise.

Hereinafter, a method and apparatus for optimizing a simulation model using a component-based dynamic model reconfiguration according to an embodiment of the present invention will be described.

1 is a diagram illustrating a structure of a simulation model optimizing apparatus according to an embodiment of the present invention.

1, a simulation model optimizing apparatus 100 according to an exemplary embodiment of the present invention includes a model optimizer 110, an optimal combination searcher 120, a dynamic model combiner 130, an optimal component generator 140, and a database (DB) unit 150. The database unit 150 includes an optimization history DB 151 and a component DB 152.

The model optimization evaluator 110 executes the simulation model and performs a model optimization evaluation based on the execution result. Specifically, the simulation execution result is compared with the optimization target to evaluate whether or not the optimization goal is achieved.

The optimal combination searcher 120 obtains the optimal combination of components and parameters to achieve the optimization goal. The optimization combination explorer 120 may be provided with the optimization target requested by the user through the user interface. The simulation model consists of a combination of components. For the combination, the relationship of the components' interfaces must be confirmed. Based on the modular characteristics of these components, the optimization combination searcher 120 can search the component DB 152 for components applicable to the specific model to obtain optimal combination of components.

The dynamic model combiner 130 constitutes a simulation model. The dynamic model combiner 130 constructs a simulation model based on optimal combination of components and combination of parameters transmitted from the optimal combination searcher 120. The simulation model thus constructed can be passed to the model optimization evaluator 110 for execution and evaluation.

The optimum component generator 140 newly generates components necessary for achieving the optimization target. The optimum component generator 140 directly generates components necessary for achieving the optimization target using the initial optimization target, the existing optimization result data (the optimization result data stored in the optimization history DB 151), and the current component. The components generated by the optimum component generator 140 can also be stored and managed in the component DB 152. [

The optimization history DB 151 stores information on the simulation model that has achieved the optimization target. Optimization goals and component and parameter information for the simulation model that achieved these optimization goals can be stored and can be referred to as optimization result data.

The component DB 152 is a repository for storing components having modular characteristics. Components of a component DB consist of modules with independent interfaces. This will be described in more detail later.

In the embodiment of the present invention, based on the simulation model optimizing apparatus having such a structure, an optimal model acquiring process for acquiring an optimum model for achieving a given optimization goal, and a model acquiring process for acquiring model information, optimization history, , And performs an optimal component generation process to generate a component capable of achieving the current optimization goal.

2 is a flowchart of a simulation model optimization method according to an embodiment of the present invention. 3 is an operation diagram illustrating that an optimal model acquisition process and an optimal component process are performed in the simulation model optimizing apparatus according to the embodiment of the present invention.

As shown in FIGS. 2 and 3, the simulation model optimizing apparatus 100 performs an optimal model acquisition process.

To this end, an optimization target is first set (S100). Through the user interface, the optimization target required by the user is input to the optimum combination searcher 120 of the simulation model optimizing apparatus 100. Optimization goals relate to the results of the simulation model and provide quantifiable goals. For example, an optimization goal of achieving a housing penetration rate of 90% in relation to housing penetration rate may be proposed.

The dynamic model combiner 130 initially constructs a given simulation model, and the model optimization evaluator 110 executes the configured simulation model (S110, S120).

When the initial simulation model is executed and a result is generated, the model optimization evaluator 110 compares the simulation result and the optimization target to determine whether to acquire the optimum model (S130). If the simulation result E does not reach the optimization target ε and the optimization model acquisition fails, the optimum combination searcher 120 analyzes the optimization result and the simulation result to search for the optimal combination of components in the component DB 152. The optimal combination searcher 120 may also perform an optimal combination of parameters and perform an optimal combination of parameters based on the optimal combination of components (S140).

FIG. 4 is a diagram illustrating a process of searching and confirming a component in a DB that can be utilized in model reconstruction in an optimal model combiner according to an embodiment of the present invention.

The components stored in the component DB 152 are composed of modules having independent interfaces. For example, as in FIG. 4, a component (e.g., component D) stored in the component DB 152 has input and output interfaces In_A and Out_B. Using the modular of these components, components are clustered on the basis of input and output interfaces to identify components from the component DB 152 that can be utilized for optimal model combination. For example, as shown in FIG. 4, when the existing simulation model is composed of the components A, B, and C, if the simulation result does not satisfy the optimization target, the component reconfiguration is performed. At this time, component C can be replaced, and replaceable components (component E, component H) are identified from the component DB 152 based on the input and output interfaces "In_C, Out_C" of component C. This input and output interface-based clustering provides component identification and component reconfiguration. In addition to the direct input and output interface relationships, this concept is also applicable to conceptual clustering such as ontology.

On the other hand, the model has several parameters, and each parameter has a range of values. The optimum parameter combination is obtained by analyzing the relation between the parameter combination and the simulation result based on the result of executing the simulation by variously changing the combination of each parameter value to find the optimum parameter combination that matches the optimization target of the model. Design of experiments can be applied to this process, and factorial design and response surface design can be applied.

As described above, when the optimal component combination and parameter combination is searched, the optimal combination searcher 120 searches the optimal combination of components and parameter combinations that have been searched, as shown in FIGS. 2 and 3, to the dynamic model combiner 130 And the dynamic model combiner 130 constructs a simulation model based on the optimum component combination and parameter combination. The dynamic model combiner 130 can construct a simulation model by performing partial component and parameter exchange in an existing simulation model based on optimal combination of components and parameter combinations (S150). In this case, the simulation execution time can be shortened by exchanging the dynamic component with the existing simulation model. Also, when the simulation model is reconstructed, the type of components, the number of components, or the connection method of components may be changed.

Based on the new simulation model configured by the dynamic model combiner 130, the model optimization estimator 110 executes the configured simulation model to perform the model optimization evaluation again.

In accordance with the repetition of this process, when the simulation result E reaches the optimization target?, The optimum model is obtained (S160). Information on the simulation model can be stored and managed in the optimization history DB 151 in accordance with the acquisition of the optimum model.

According to the embodiment of the present invention, the optimization of the simulation model can be performed by searching the combination of the parameter and the component model and considering the higher-dimensional search range. This provides a variety of model optimization methods.

Meanwhile, in the embodiment of the present invention, various simulation model optimization can be performed by presenting a new component in consideration of an optimization history.

5 is a diagram illustrating component registration and addition in accordance with an embodiment of the present invention.

There are two ways to register a component in the component DB. Basically, the first method is a method in which a user directly creates a component and registers it as an API (application program interface) provided by the component DB. The second method is a method of registering a new component through generation of an optimal component according to an embodiment of the present invention.

In an embodiment of the present invention, using the initial optimization goals, existing optimization result data (optimization result data stored in the optimization history DB 151), and the current component, the optimal component generator 104 achieves the optimization target Create the required components directly. To do this, data-based clustering and reasoning techniques that are covered in machine learning and artificial intelligence can be utilized. For example, the influence of simulation results on model parameter and structural information changes is analyzed to analyze the correlation between specific parameters, structure, and simulation results. In this process, artificial neural network or deep running method can be applied. By using the artificial intelligence model, we can design the parameters and structure of the components that require specific simulation results.

Thus, the newly generated component by the optimal component generator 104 can be stored and managed in the component DB 152 and is also provided to the dynamic model combiner 130 and used for the simulation model reconstruction.

6 is a structural diagram of a simulation model optimizing apparatus according to another embodiment of the present invention.

6, the simulation model optimizing apparatus 200 according to the embodiment of the present invention includes a processor 210, a memory 220, and an input / output unit 230. The processor 210 may be configured to implement the methods described above based on Figs. 1-5. To this end, the processor 210 may be configured to include a model optimization estimator, an optimal combination searcher, a dynamic model combiner, and an optimal component generator.

The memory 220 is coupled to the processor 210 and stores various information related to the operation of the processor 210. The memory 220 stores instructions for an operation to be performed by the processor 210 or may temporarily store an instruction loaded from a storage device (not shown). The memory 220 may be configured to include, for example, an optimization history DB and a component DB. The optimization history DB and the component DB may be implemented in a form contained in an individual memory.

The processor 210 may execute instructions that are stored or loaded into the memory 220. The processor 210 and the memory 220 are connected to each other via a bus (not shown), and an input / output interface (not shown) may be connected to the bus.

The input / output unit 230 is configured to output the processing result of the processor 210 or to receive data input through the user interface and to provide the data to the processor 210.

The embodiments of the present invention are not limited to the above-described apparatuses and / or methods, but may be implemented through a program for realizing functions corresponding to the configuration of the embodiment of the present invention, a recording medium on which the program is recorded And such an embodiment can be easily implemented by those skilled in the art from the description of the embodiments described above.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, It belongs to the scope of right.

Claims (12)

As a method for optimizing a simulation model,
The apparatus comprising: setting an optimization target;
The apparatus comprising: initially executing a predetermined simulation model;
The apparatus comprising: comparing the result of the simulation model execution with the optimization target;
Searching for a combination of components suitable for the simulation model if the result of the execution of the simulation model does not satisfy the optimization target and searching for a combination of parameters suitable for the simulation model;
Reconstructing the simulation model based on the searched component combination and parameter combination; And
Executing the reconstructed simulation model
Lt; / RTI >
And an optimal simulation model is obtained based on a comparison of the result of executing the reconstructed simulation model and the optimization goal. The method according to claim 1,
Determining a combination of the parameters until a result of the execution of the simulation model satisfies the optimization target, reconstructing the simulation model, executing the reconstructed simulation model, and comparing the optimization goals Lt; / RTI > The method according to claim 1,
Wherein reconstructing the simulation model comprises:
And performing a dynamic exchange of components of the simulation model based on the searched component combination to reconstruct the simulation model. The method according to claim 1,
Wherein the step of finding a combination of the parameters comprises:
Identifying a component to be utilized by clustering components based on input and output interfaces from a database of components comprising modules having independent input and output interfaces;
/ RTI > The method according to claim 1,
Wherein the step of finding a combination of the parameters comprises:
Generating a new component for achieving the optimization goal using the optimization goal, existing optimization result data, and the components that make up the simulation model
/ RTI > 6. The method of claim 5,
Wherein reconstructing the simulation model comprises reconstructing the simulation model using the newly generated component,
Wherein the optimization result data is optimization history information including component and parameter information for a simulation model that achieves any optimization goal. An apparatus for optimizing a simulation model,
An input / output unit configured to receive input of data; And
A processor coupled to the input / output unit and configured to perform simulation model optimization,
The processor comprising:
Searching for a combination of components suitable for the simulation model and searching for a combination of parameters suitable for the simulation model when the result of executing the initial simulation model does not satisfy a preset optimization target, And reconstruct and run the simulation model based on the combination,
And an optimal simulation model is obtained based on a comparison of the result of executing the reconstructed simulation model and the optimization target. 8. The method of claim 7,
Wherein the processor is further configured to find a combination of the component and parameter suitable for the simulation model and reconstruct and execute the simulation model until the result of the execution of the simulation model satisfies the optimization target, And to perform the process of comparing the targets repeatedly to obtain the optimal simulation model. 8. The method of claim 7,
Wherein the processor is further configured to perform a dynamic exchange of components of the simulation model based on the searched component combination to reconstruct the simulation model. 8. The method of claim 7,
A component database consisting of modules having independent input / output interfaces is stored
Further comprising:
Wherein the processor is configured to cluster components based on input and output interfaces to identify components suitable for the simulation model from the component database. 8. The method of claim 7,
An optimization history database storing optimization result data, which is optimization history information including component and parameter information for a simulation model that achieves a random optimization target
Further comprising:
The processor comprising:
And generating a new component for achieving the optimization goal using the optimization target, the optimization result data, and a component that constitutes the simulation model, and reconstructing the simulation model using the newly generated component, Device. 8. The method of claim 7,
The processor comprising:
A model optimization estimator configured to execute the simulation model and to perform a model optimization evaluation by comparing the execution result and the optimization target;
An optimal combination navigator configured to obtain optimal combination of components and parameters to achieve the optimization goal;
A dynamic model combiner for reconstructing the simulation model based on the combination of components and parameters; And
An optimal component generator configured to newly generate a component required to achieve the optimization target,
. ≪ / RTI >
KR1020170160180A 2017-11-28 2017-11-28 Method and apparatus for simulation model optimization using component-based dynamic model reconstruction KR20190061616A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112612772A (en) * 2020-12-01 2021-04-06 中车长江车辆有限公司 Polyurethane foaming simulation database construction method and device
KR20210117030A (en) * 2020-03-18 2021-09-28 인하대학교 산학협력단 Digital twin system and method for virtualization of autonomous driving
CN114861403A (en) * 2022-04-12 2022-08-05 国网江苏省电力有限公司电力科学研究院 Dynamic simulation step length optimization method and system for electrothermal coupling network
CN117473932A (en) * 2023-10-27 2024-01-30 华南理工大学 Agile design library driven DTCO efficient optimization method

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20210117030A (en) * 2020-03-18 2021-09-28 인하대학교 산학협력단 Digital twin system and method for virtualization of autonomous driving
CN112612772A (en) * 2020-12-01 2021-04-06 中车长江车辆有限公司 Polyurethane foaming simulation database construction method and device
CN112612772B (en) * 2020-12-01 2023-07-18 中车长江车辆有限公司 Database construction method and device for polyurethane foaming simulation
CN114861403A (en) * 2022-04-12 2022-08-05 国网江苏省电力有限公司电力科学研究院 Dynamic simulation step length optimization method and system for electrothermal coupling network
CN114861403B (en) * 2022-04-12 2023-10-27 国网江苏省电力有限公司电力科学研究院 Electric heating coupling network dynamic simulation step length optimization method and system
CN117473932A (en) * 2023-10-27 2024-01-30 华南理工大学 Agile design library driven DTCO efficient optimization method

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