CN115130332A - Simulation method and device for efficiency evaluation system - Google Patents
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Abstract
The application discloses a simulation method and device for an efficiency evaluation system, which comprises the following steps: acquiring evaluation requirements to be evaluated, which are oriented to an efficiency evaluation system, wherein the evaluation requirements at least comprise an evaluation task, an evaluation condition and an evaluation target to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining simulation models corresponding to the plurality of acquisition points according to the parameter information of the plurality of acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to the comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a simulation method and apparatus, a terminal device, and a storage medium for an efficiency evaluation system.
Background
In the research of simulation and performance evaluation, it has become increasingly important to adopt simulation means to obtain data required for evaluation. Due to the lack of clear evaluation requirement model support, data acquired in many simulation systems cannot meet the requirement of performance evaluation, and even a large amount of data with little significance to performance evaluation occurs, so that the cracking phenomenon that simulation and evaluation are difficult to reflect mutually and support mutually is caused.
Disclosure of Invention
The invention aims to provide a simulation method, a simulation device, terminal equipment and a storage medium for an efficiency evaluation system, so as to solve the defects in the prior art.
In a first aspect, an embodiment of the present invention provides a simulation method for a performance evaluation system, where the method includes:
acquiring evaluation requirements to be evaluated, which are oriented to a performance evaluation system, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved;
determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points;
determining simulation models corresponding to the acquisition points according to the parameter information of the acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results;
and receiving a simulation result returned by the simulation model, comparing the simulation result with the evaluation requirement, and performing self-adaptive adjustment on the simulation model according to the comparison result.
Optionally, the determining a simulation acquisition order according to the evaluation requirement includes:
acquiring a top-level index in an evaluation target;
gradually unfolding and decomposing the top layer indexes into lower layer indexes until the lower layer indexes carry characteristic information, wherein the lower layer indexes are used for calculation on a simulation model;
and determining the parameter information of each acquisition point on the simulation acquisition list according to the lower layer index.
Optionally, the receiving a simulation result returned by the simulation model includes:
receiving a simulation result obtained after the simulation model carries out data acquisition, storage and access operations according to the parameter information of the simulation acquisition list on the acquisition list;
and performing measurement analysis on the evaluation index according to the simulation result to obtain an evaluation result.
Optionally, the method further comprises:
comparing the simulation result with the evaluation result to obtain a comparison result;
and if the comparison result does not meet the evaluation requirement, adjusting the simulation excitation.
Optionally, the comparing the simulation result and the evaluation result to obtain a comparison result includes:
respectively adopting one or more of the following comparison modes for comparison to obtain comparison results;
wherein the comparison result at least comprises the comparison of the precision of the simulation result and the comparison of the simulation result with the optimal solution.
Optionally, the adaptively adjusting the simulation model according to the comparison result includes:
adopting a genetic algorithm to adjust the self-adaptive simulation excitation to obtain an adjustment result;
and performing self-adaptive adjustment on each simulation model according to the adjustment result.
Optionally, the format of the simulation acquisition order is a data format convenient for editing and understanding, and the simulation acquisition order at least comprises a mapping relation between each index and an acquisition point.
In a second aspect, an embodiment of the present invention provides a simulation apparatus for a performance evaluation system, where the apparatus includes:
the system comprises an acquisition module, a performance evaluation module and a performance evaluation module, wherein the acquisition module is used for acquiring evaluation requirements to be evaluated, which are oriented to a performance evaluation system, and the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved;
the determining module is used for determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points;
the simulation module is used for determining simulation models corresponding to the acquisition points according to the parameter information of the acquisition points so as to enable the simulation models to simulate at the acquisition points to obtain a simulation result;
and the receiving module is used for receiving the simulation result returned by the simulation model, comparing the simulation result with the evaluation requirement and performing self-adaptive adjustment on the simulation model according to the comparison result.
Optionally, the determining module is configured to:
acquiring a top-level index in an evaluation target;
gradually unfolding and decomposing the top layer indexes into lower layer indexes until the lower layer indexes carry characteristic information, wherein the lower layer indexes are used for calculation on a simulation model;
and determining the parameter information of each acquisition point on the simulation acquisition list according to the lower layer index.
Optionally, the receiving module is configured to:
receiving a simulation result obtained after the simulation model carries out data acquisition, storage and access operations according to the parameter information of the simulation acquisition list on the acquisition list;
and performing measurement analysis on the evaluation index according to the simulation result to obtain an evaluation result.
Optionally, the receiving module is configured to:
comparing the simulation result with the evaluation result to obtain a comparison result;
and if the comparison result does not meet the evaluation requirement, adjusting the simulation excitation.
Optionally, the receiving module is specifically configured to:
respectively adopting one or more of the following comparison modes for comparison to obtain comparison results;
wherein the comparison result at least comprises the comparison of the precision of the simulation result and the comparison of the simulation result with the optimal solution.
Optionally, the receiving module is configured to:
adopting a genetic algorithm to adjust the self-adaptive simulation excitation to obtain an adjustment result;
and carrying out self-adaptive adjustment on each simulation model according to the adjustment result.
Optionally, the format of the simulation acquisition list is a data format convenient for editing and understanding, and the simulation acquisition list at least includes a mapping relation between each index and an acquisition point.
In a third aspect, an embodiment of the present invention provides a terminal device, including: at least one processor and memory;
the memory stores a computer program; the at least one processor executes the computer program stored in the memory to implement the performance evaluation system-oriented simulation method provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed, the simulation method for a performance evaluation system provided in the first aspect is implemented.
The embodiment of the invention has the following advantages:
according to the simulation method and device, the terminal device and the storage medium for the performance evaluation system, evaluation requirements for the performance evaluation system to be evaluated are obtained, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining simulation models corresponding to the plurality of acquisition points according to the parameter information of the plurality of acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to the comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions of the present application, the drawings needed for describing the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings can be obtained by those skilled in the art without inventive exercise.
Fig. 1 is a flowchart of a simulation method for a performance evaluation system according to an embodiment of the present application;
FIG. 2 is a flow chart of an adaptive simulation method for performance evaluation according to an embodiment of the present application;
FIG. 3 is a flow chart of a simulation and evaluation framework supporting adaptive adjustment of acquisition points in an embodiment of the present application;
FIG. 4 is a diagram illustrating external features of an exemplary simulation input adaptive construction technique according to the present application;
FIG. 5 is a schematic structural diagram of a simulation apparatus for a performance evaluation system according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device according to the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following embodiments and accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
An embodiment of the present invention provides a simulation method for an efficiency evaluation system, which is used for performing performance evaluation on the efficiency evaluation system to be evaluated. The execution subject of this embodiment is a simulation apparatus oriented to the performance evaluation system, and is disposed on a terminal device, for example, the terminal device at least includes a computer terminal.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a simulation method for a performance evaluation system according to the present invention is shown, where the method specifically includes the following steps:
s101, obtaining evaluation requirements to be evaluated, which are oriented to an efficiency evaluation system, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved;
specifically, the embodiment of the invention provides a simulation data acquisition technology based on a simulation data acquisition sheet, and establishes an organic connection between a simulation loop and an evaluation loop. The simulation data acquisition sheet is just a reflection of the performance evaluation requirement on data acquisition. In addition, the calculation of each final-stage index in the evaluation index system needs a simulation process to provide a specific parameter value result, and in a specific simulation, an evaluator usually only focuses on one or more specific indexes, so that if all acquisition points are acquired in each simulation, the acquisition is unnecessary, and the adjustment of simulation input is not facilitated. Therefore, the simulation data acquisition technology is required to automatically adjust the acquisition points according to the change of the observed indexes.
In the performance-oriented evaluation and simulation, the system comprises three modules, namely an evaluation system, a simulation system and a medium-simulation data acquisition module between the evaluation system and the simulation system. The evaluation system reflects the evaluation requirement by drawing up an evaluation index system, and the key of evaluation index calculation is that the support of specific parameter values of the final-stage index of the evaluation index system is needed;
the evaluation demander provides a required data list, namely a collection list, which can provide evaluation, and the simulation system needs to finally reflect what purpose, namely, the detailed evaluation requirement. The performance evaluation-oriented simulation system needs to obtain data that can clearly reflect performance indexes in simulation results. The simulation modeling personnel are required to carry out corresponding negotiation according to the evaluation requirement, confirm and draw up a proper simulation acquisition list, and consciously consider relevant modules in the simulation modeling.
The purpose of the performance evaluation is to evaluate a certain capability degree of the system through a set of evaluation index system, so as to provide decision support for system design, deployment and the like. In evaluating requirements, it is necessary to determine mission tasks of the system, understand evaluation conditions, and achieve evaluation goals. Determining an evaluation index system according to evaluation requirements is an important task, generally, a project level target (namely a top level index) surrounding performance evaluation needs to be gradually expanded and decomposed into a lower level index, generally, the index is required to be decomposed into an index capable of reflecting a certain characteristic of performance, and meanwhile, the lower level index is required to be capable of being directly analyzed and calculated on simulation data. From the simulation point of view, the performance evaluation index system provides data required to be provided by the simulation, and belongs to the requirements of the evaluation system on the simulation.
S102, determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points;
the evaluation index system provides clear data requirements for evaluation, and how to automatically acquire corresponding data from the simulation system is a problem to be solved by the step. Generally, data acquisition focuses on the final index, and the upper index can be calculated from the lower index to obtain a measurement result. Due to the complexity of the actual system and the diversification of the evaluation requirements, the data required by the evaluation index can not be collected from the simulation system, for example, some simulation systems may need the operation condition of the system in some experimental environments, and the simulation system cannot simulate the environment. At this time, the simulation modeler and the evaluation modeler need to negotiate and discuss to analyze whether the final index decomposition of the evaluation index system is proper, the feasibility of the data obtained in the simulation model or the current simulation technology, and the like.
S103, determining simulation models corresponding to the plurality of acquisition points according to the parameter information of the plurality of acquisition points, so that the simulation models can simulate at the acquisition points to obtain a simulation result;
the evaluation party needs the simulation party to acquire, store and access data according to the data acquisition requirement on the acquisition list so as to carry out measurement analysis of the evaluation index. And the simulation party builds a simulation model according to the data acquisition points mentioned in the data acquisition list or sets a data acquisition module in the existing simulation model to support the efficiency evaluation. Simulation data collectors in a uniform format need to be established in the simulation model.
And S104, receiving a simulation result returned by the simulation model, comparing the simulation result with the evaluation requirement, and performing self-adaptive adjustment on the simulation model according to the comparison result.
In the operation process of the simulation system, aiming at a specific observation target, the simulation excitation is dynamically adjusted according to the output response and the characteristics of the simulation, and effective input events are continuously generated, so that the output result is continuously improved.
According to the embodiment of the invention, by establishing the simulation model with dynamic adjustment behavior description capability, the simulation data acquisition technology supporting self-adaptive adjustment of acquisition points and the self-adaptive construction technology of simulation excitation, on one hand, the dynamic change behavior of a system with self-adaptability is supported and described in the simulation process, and on the other hand, the self-adaptive adjustment of simulation input is supported, so that the output result is continuously improved, and the optimization of the performance of the simulation system is further realized.
According to the simulation method for the performance evaluation system, provided by the embodiment of the invention, evaluation requirements for the performance evaluation system to be evaluated are obtained, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining a simulation model corresponding to the multiple acquisition points according to the parameter information of the multiple acquisition points, so that the simulation model can simulate at the acquisition points to obtain a simulation result; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to the comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
The present invention further provides a supplementary description of the simulation method for a performance evaluation system provided in the above embodiment.
Optionally, determining a simulation acquisition order according to the evaluation requirement includes:
acquiring a top-level index in an evaluation target;
gradually unfolding and decomposing the top layer indexes into lower layer indexes until the lower layer indexes carry characteristic information, wherein the lower layer indexes are used for calculation on a simulation model;
and determining the parameter information of each acquisition point on the simulation acquisition list according to the lower-layer index.
Optionally, receiving a simulation result returned by the simulation model, including:
receiving a simulation result obtained after a simulation model carries out data acquisition, storage and access operations according to parameter information of a simulation acquisition list on the acquisition list;
and performing measurement analysis on the evaluation index according to the simulation result to obtain an evaluation result.
Optionally, the method further comprises:
comparing the simulation result with the evaluation result to obtain a comparison result;
and if the comparison result does not meet the evaluation requirement, adjusting the simulation excitation.
Optionally, comparing the simulation result with the evaluation result to obtain a comparison result, including:
respectively adopting one or more of the following comparison modes for comparison to obtain comparison results;
wherein the comparison result at least comprises the comparison of the precision of the simulation result and the comparison of the simulation result with the optimal solution.
Optionally, the adaptively adjusting the simulation model according to the comparison result includes:
adopting a genetic algorithm to adjust the self-adaptive simulation excitation to obtain an adjustment result;
and carrying out self-adaptive adjustment on each simulation model according to the adjustment result.
Optionally, the format of the simulation acquisition list is a data format convenient for editing and understanding, and the simulation acquisition list at least comprises a mapping relation between each index and an acquisition point.
Fig. 2 is a flowchart of an adaptive simulation method for performance evaluation according to an embodiment of the present application, as shown in fig. 2: the simulation model with the dynamic adjustment behavior description capability is included;
in the course of intensive analysis and research, it was proposed to apply DSDEVS to describe and construct complex systems with adaptivity.
Two models are included in DSDEVS: the system comprises a Basic Model (Basic Model) and a Network Model (Network Model), wherein the Basic Model is used for describing a non-separable module behavior including input and output events, state transition and state retention time, the Network Model is used for describing an external behavior of an aggregation module and an interaction behavior between internal modules of the aggregation module including the input and output events, a topological structure of sub-module connection, the state transition and the state retention time, and the Network Model can refer to the Basic Model or another Network Model so as to construct a structure and behavior Model of the whole system in a modularized and hierarchical mode.
The base model corresponds to the atomic model in classical DEVS, whose formalization is defined as follows:
Xa set of external input events;
Sa set of system states;
Youtputting the event set;
ta is the time advance function. ta(s) represents the time for which the state of the system remains at s when no external event arrives, and the state of ta(s) = + ∞, in which the system will stay if no external event arrives, is called stationary; the state ta(s) =0 is called transient, i.e. the emulated clock does not advance while this state is executing.
The network model can be defined as< 
, 
>Wherein
Is an execution body that is to execute the program,
is that
Can be regarded as a special basic model:
wherein
Is a set of states, each of which
Are defined as follows:
a)X、Yrespectively representing an external input event set and an output event set;
b)D: a set of member model names;
c) for theDEach member ofi，M i Is thatiThe corresponding member model is used to represent the member model,I i is a member of receptioniA set of affected members;
d) for theI i Each member ofj，Z ij Is a memberiTo the memberjThe transfer function of the input of (a);
e)Selectthe function being a selection of multiple members with simultaneous state transitionsA function;
f)θ: a newly added set of parameters in the topology.
Thus, the state set
Each state in (a) represents a network topology when
When the state of the model is changed, the structure of the model is correspondingly changed, thereby showing the dynamic change of the structure.
On the basis of the above DSDEVS formal definition, a method of constructing a simulation model conforming to the DSDEVS specification and an implementation of a simulation engine thereof have been studied. The user can construct a simulation model of the system, which conforms to the DSDEVS specification, by inheriting Atomic and Coupled classes in the base class diagram. The definition of the main elements in the basic class diagram is as shown in tables 1 to 12:
table 1 DSDEVS basic implementation class diagram element description
| Element name | Properties | Brief description of the drawings |
| Component | Model (model) | Describing the DSDEVS model, is a parent of all models |
| Atomic | Atomic model | Describing the basic model in DSDEVS, the same as the atomic model definition in classical DEVS |
| Coupled | Network model | Describing network models in DSDEVS |
| Coupling | Associated | Describing information exchange and association relation between models |
| PortInterface | Port(s) | Model describing port |
| StructModifyInfo | Configuration change information | Structural dynamic change information describing a network model |
TABLE 2 Component element description
| Element name | Properties | Brief description of the drawings |
| Name | Name (R) | Name of model |
| Inports | Input port set | All input ports of the description model |
| Outports | Output port set | All output ports of the description model |
TABLE 3 Atomic element description
| Element name | Properties | Brief description of the drawings |
| Phase | Status of state | Describing the current state of the base model |
TABLE 4 summary element description
| Element name | Properties | Brief description of the drawings |
| Components | Set of submodules | Sub-module composition for describing network model |
| Ic | Internal association set | Describing associations between all internal sub-modules of a network model |
| Eic | Input association set | Describing all associations of network model from input port to submodule |
| Eoc | Outputting association sets | Describing all associations of network model from sub-module to output port |
Table 5 Coupling element description
| Element name | Properties | Brief description of the drawings |
| ComponentFrom | Source model | Sub-module composition for describing network model |
| ComponentTo | Purpose model | Describing associations between all internal sub-modules of a network model |
| PortFrom | Source port | Describing all associations of network model from input port to submodule |
| PortTo | Destination port | Describing all associations of network model from sub-module to output port |
Table 6 PortInterface element specification
| Element name | Properties | Brief description of the drawings |
| Name | Name (R) | Port name |
TABLE 7 structModifyInfo element Specification
| Element name | Properties | Brief description of the drawings |
| Component | Model (model) | Describing models of structures to be changed |
| Flag | Model change patterns | Describing change patterns of a model |
Table 8 SDEVS simulation engine element specification
| Element name | Properties | Brief description of the drawings |
| DevsSimulator | Emulator | Describing the DSDEVS model, is a parent of all models |
| Simulator | Atom model simulator | Describing base models in DSDEVS |
| Coordinator | Network model simulator | Describing network models in DSDEVS |
| ExecutiveSimulator | Executive simulator | Describing information exchange and association relation between models |
TABLE 9 DevsSimulator class Attribute/method Explanation
| Attribute/method | Name (R) | Brief description of the drawings |
| initialize() | Initialization | Simulation clock initialization |
| lamda() | Output function | Output function when no external event occurs when clock arrives |
| deltfcn() | State transfer function | Describing how states change |
| getComponent() | Obtaining a model | Describe which model corresponds to |
TABLE 10 Simulator class Attribute/method Specification
| Attribute/method | Name (R) | Brief description of the drawings |
| Model | Model (model) | Describing which atomic model corresponds to |
TABLE 11 Coordinator class Attribute/method Specification
| Attribute/method | Name (R) | Brief description of the drawings |
| Model | Model (model) | Describing which network model corresponds to |
| Simulators | Simulator list | Simulator list corresponding to description sub-model |
| buildHierarchy() | Layering | A simulator is newly built for each sub-model, and a sub-simulator list is built |
| simulate() | Simulation (Emulation) | Start-up emulation |
| propagateInput() | Input transfer | Passing model inputs to submodels |
| moveDataBetweenPorts() | Data transfer | Messaging between models |
| propagateOutput() | Output delivery | Passing the output of the model to the next model |
TABLE 12 Executive Simulator class Attribute/method Specification
| Attribute/method | Name (R) | Brief description of the drawings |
| Model | Model (model) | Describing which atomic model corresponds to |
| Coordinator | Emulator | Simulator for describing corresponding network model |
| ExecutiveSimulator () | Executive simulator | Constructor function |
| changeStructure () | Changing the structure | Controlling structural changes of a network model |
Coordinator is a core component of the overall architecture from which the engine enters an operational state when it returns a message to start operation. The engine creates a devsssimulator for each sub-model in the network model (building a Simulator for the basic model and a Coordinator for the network model), puts in a timer list, and turns to the initial state. And (3) with the stepping of the simulation clock, the engine continuously judges which model carries out state transition at the current moment by checking the timer list, executes the timer operation defined by the state, updates the timer list after the execution is finished, and carries out the next stepping. Meanwhile, the engine defines an executive simulator for the network model with the capability of dynamically adjusting the structure, and is used for receiving the structure change information of the network model, when the executive simulator receives an abstract excitation event sent by the model, the correlation between the modules of the simulation model and the modules is dynamically changed, the simulation stepping is continuously carried out until a termination state is reached, the operation is stopped, and the engine enters the termination state.
In the research of simulation and performance evaluation, it has become increasingly important to adopt simulation means to obtain data required for evaluation. Due to the lack of clear support of an evaluation requirement model, data acquired in a plurality of simulation systems cannot meet the requirement of performance evaluation, and even a large amount of data with low significance to the performance evaluation appears, so that the cracking phenomenon that simulation and evaluation are difficult to reflect mutually and support mutually is caused. Therefore, a simulation data acquisition technology based on a simulation data acquisition sheet is designed, and organic connection between a simulation loop and an evaluation loop is established. The simulation data acquisition sheet is just a reflection of the performance evaluation requirement on data acquisition. In addition, the calculation of each final-stage index in the evaluation index system needs a simulation process to provide a specific parameter value result, and in a specific simulation, an evaluator usually only focuses on one or more specific indexes, so that if all acquisition points are acquired in each simulation, the acquisition is unnecessary, and the adjustment of simulation input is not facilitated. Therefore, the simulation data acquisition technology is required to automatically adjust the acquisition points according to the change of the observed indexes.
FIG. 3 is a flow chart of a simulation and evaluation framework supporting adaptive adjustment of acquisition points in an embodiment of the present application; in order to enable the simulation data acquisition technology to support adaptive adjustment of acquisition points, the simulation data acquisition list is based on the fact that the estimation requirement puts forward a simulation data list needing to be acquired to a simulation system in simulation and estimation. It is used as an intermediate medium for simulation and evaluation, and plays a role in coordination and cooperation between the simulation and the evaluation.
As shown in fig. 3, the performance-oriented evaluation and simulation system includes three modules, namely an evaluation system, a simulation system, and a simulation data acquisition module, which is a medium between the evaluation system and the simulation system. The evaluation system reflects the evaluation requirement by drawing up an evaluation index system, and the key of evaluation index calculation is that the support of specific parameter values of the final-stage indexes of the evaluation index system is needed. Therefore, the evaluation requirement side provides a required data list, namely a collection list, which can provide evaluation, and the simulation system needs to finally reflect what purpose and see detailed evaluation requirements. The performance evaluation-oriented simulation system needs to obtain data that can clearly reflect performance indexes in simulation results. The simulation modeling personnel are required to carry out corresponding negotiation according to the evaluation requirement, confirm and draw up a proper simulation acquisition list, and consciously consider relevant modules in the simulation modeling. The simulation data acquisition module has the function of maintaining a simulation data acquisition list and acquiring data according to acquisition points specified on the acquisition list in the simulation process. The acquisition sheet records the mapping from each final-stage index in the evaluation index system to the acquisition point, so that the data acquisition point can be adaptively adjusted according to the index to be observed during simulation, and all the acquisition points are prevented from being acquired. Through the design, the simulation data acquisition sheet can establish organic connection between the simulation loop and the evaluation loop, so that both the simulation and the evaluation can cooperatively carry out respective work based on the common simulation acquisition sheet, the simulation modeling purpose can be enhanced, the simulation modeling work is more specific and effective, and the acquisition points can be adaptively adjusted during data acquisition in the simulation process.
According to the framework diagrams of the evaluation system, the simulation system and the simulation data acquisition module, the construction steps of the related simulation acquisition list can be provided in combination with the general practice of performance evaluation.
(1) And (5) determining the evaluation requirement and establishing an evaluation index system.
The purpose of the performance evaluation is to evaluate a certain capability degree of the system through a set of evaluation index system, so as to provide decision support for system design, deployment and the like. In evaluating the requirements, the mission task of the system needs to be determined, the evaluation conditions are understood, and the evaluation target is to be achieved. Determining an evaluation index system according to evaluation requirements is an important task, generally, a project level target (namely a top level index) surrounding performance evaluation needs to be gradually expanded and decomposed into a lower level index, generally, the index is required to be decomposed into an index capable of reflecting a certain characteristic of performance, and meanwhile, the lower level index is required to be capable of being directly analyzed and calculated on simulation data. From the simulation point of view, the performance evaluation index system provides data required to be provided by the simulation, and belongs to the requirements of the evaluation system on the simulation.
(2) And converting the final-stage index into a simulation data acquisition list.
The evaluation index system provides clear data requirements for evaluation, and how to automatically acquire corresponding data from the simulation system is a problem to be solved by the step. Generally, data acquisition focuses on the final index, and the upper index can be calculated from the lower index to obtain a measurement result. Due to the complexity of the actual system and the diversification of the evaluation requirements, the data required by the evaluation index can not be collected from the simulation system, for example, some simulation systems may need the operation condition of the system in some experimental environments, and the simulation system cannot simulate the environment. At this time, the simulation modeler and the evaluation modeler need to negotiate and discuss to analyze whether the final index decomposition of the evaluation index system is proper, the feasibility of the data obtained in the simulation model or the current simulation technology, and the like. If the data required for evaluating the final indexes in the index system cannot be automatically obtained in the simulation system, the evaluation modeler needs to provide other alternatives, such as adjusting the design of the index system, or acquiring corresponding data from other data sources. And finally, a set of simulation data acquisition list is finalized through repeated negotiation.
(3) And distributing the parameters to corresponding simulation models according to the parameters of the simulation acquisition list.
The evaluator needs the simulator to collect, store and access data according to the data collection requirement on the collection sheet so as to carry out measurement analysis of the evaluation index. And the simulation party builds a simulation model according to the data acquisition points mentioned in the data acquisition list or sets a data acquisition module in the existing simulation model to support the efficiency evaluation. Simulation data collectors in a uniform format need to be established in the simulation model.
In order to enable the simulation data process to support the adaptive adjustment of the acquisition points, a certain simulation modeling specification must be specified.
The final indexes of the evaluation index system are basic indexes which are acquired from simulated original data and subjected to a large amount of simple statistical processing, the basic indexes are basic references of data acquisition work, and each final index must have a unique number.
For a simulation modeler, index mapping in the acquisition list is a requirement on data acquisition work of a simulation model, self capacity and technology need to be considered when the mapping is established, and the mapping is negotiated with evaluators to discuss whether a final index can be acquired. All data collectors in the simulation model are determined. In order to support the self-adaptive adjustment of the acquisition points, a data collector of the simulation model also has a unique number and is provided with a switch mark, and only the acquisition points corresponding to the observation indexes are acquired.
The format of the acquisition list preferably adopts a data format which is convenient to edit and understand so as to facilitate the communication and discussion of evaluators and simulation modelers and facilitate the work of both parties. For example, a 0-1 matrix is used for representing the mapping from the index to the acquisition point, the row of the matrix represents the index, the column represents the acquisition point, the acquisition point related to a certain index is assigned with 1, and if not, the acquisition point is assigned with 0, so that the mapping relation between the index and the acquisition point is recorded. When an evaluator selects to observe a specific index, the simulation model reads a row corresponding to the index to control the switch of the corresponding acquisition point for simulation.
FIG. 4 is a diagram illustrating external features of an exemplary simulation input adaptive construction technique according to the present application; the researched simulation excitation adaptive construction technology can dynamically adjust the simulation excitation according to the output response and the characteristics of the simulation in the operation process of the simulation system aiming at a specific observation target, and continuously generates effective input events, thereby continuously improving the output result. Therefore, the essence of adaptive simulation is to establish a feedback loop for the conventional simulation technique, as shown in FIG. 4. The simulation input self-adaptive construction strategy utilizes the output of the simulation model to provide feedback for the searching process of the optimal solution, and the process of solving the optimal value can be quitted when the generated output result meets the optimizing condition.
The purpose of the adaptive simulation method and technique provided by the embodiments of the present invention is to obtain system dynamic behavior simulation data for performance evaluation, and therefore, validity and correctness of a simulation result need to be verified. Aiming at the performance evaluation requirement and the technical characteristics of the integrated electronic information system, the following analysis and verification indexes are designed in the embodiment of the application:
1) adjustment success rate
The self-adaptive adjustment of simulation input in the simulation process can cause the change of a simulation result, and whether the simulation result can meet the requirement of an index system on efficiency evaluation determines whether to stop adjusting the simulation excitation or not. When the output result reaches the set precision and meets the optimizing condition, the adjusting process is considered to be successful. The embodiment of the application evaluates the success rate of the self-adaptive adjustment of the simulation input by using the percentage of the times that the result obtained in the maximum iteration times reaches the set precision to the total operation times. Assuming that N is the total number of runs of the simulation experiment,
indicating the accuracy of the simulation result of the ith time,
representing a set accuracy threshold, the index is defined as follows:
2) Adjusting efficiency
The process of simulation input self-adaptive construction is to utilize the output of the simulation model to provide feedback for the searching process of the optimal solution, and when the generated output result meets the optimizing condition, the process of adjustment can be quitted. Due to time constraints, the simulation cannot run indefinitely. Before the termination condition is met, namely before the adjustment process of the simulation input is finished, the simulation times are more, which shows that the effect of the self-adaptive adjustment is worse, and the required simulation time is longer. The adjustment efficiency refers to the number of adjustments until the termination condition is satisfied. In general, the smaller the number of simulations required, the better the tuning effect. The method and the device for the self-adaptive adjustment of the simulation input evaluate the effectiveness of the self-adaptive adjustment of the simulation input by using the index. Assuming that n is the number of iterations before the termination condition is satisfied, the index is defined as follows:
3) Adjusting convergence
The process of adaptively constructing the simulation input is a process of providing feedback for searching the optimal solution by utilizing the output of the simulation model, and when the generated output result meets the optimization condition, the process of adjustment can be quitted. And repeating the operation for multiple times, wherein the algorithm with the better average convergence value is superior to other algorithms in the same iteration times. And repeating the operation for multiple times, and if the obtained average convergence values are not different greatly in the same iteration times, comparing the fluctuation conditions of the convergence values of the algorithms, wherein the smaller the fluctuation, the better the algorithm is. The method and the device use the index to evaluate the convergence of the simulation input self-adaptive adjustment. Assuming that N represents the number of adjustment test runs,
indicating the accuracy of the ith trial at a particular number of iterations, the indices are defined as follows:
Adaptive simulation strategy selection
The genetic algorithm is a search algorithm with robustness which can be used for complex system optimization, is a highly parallel, random and self-adaptive optimization algorithm based on survival of the fittest, expresses the solution of the problem as the survival process of the fittest of the chromosome, and finally converges to the optimal solution of the problem through continuous evolution of the chromosome group, including replication, intersection and variation. It is characterized in that: the problem parameters are coded into chromosomes and then are subjected to quantization operation, and the parameters are not targeted, so that the problem parameters are not limited by function constraint conditions; the searching process starts from a set of problem solutions instead of a single body, and has the characteristic of implicit parallel searching, so that the possibility of trapping in local minimum is greatly reduced; the genetic manipulation is random and searches according to individual fitness value information without other information.
The superiority of the genetic algorithm is mainly expressed in that: the algorithm carries out full-space parallel search and focuses the search key on the part with high performance, so that the efficiency is improved and the local minimum is not easy to fall; the method has the advantages of inherent parallelism, capability of processing a large number of patterns through genetic processing of the population and easiness in parallel implementation. Therefore, from the viewpoints of avoiding the possibility of falling into local minima, algorithm efficiency and the like, a genetic algorithm can be applied to the adaptive adjustment strategy of the simulation excitation, and the basic application steps of the genetic algorithm in the adaptive simulation technology are as follows:
chromosome encoding and decoding
The genetic algorithm does not operate on the actual simulation stimulus of the simulation model, so the primary problem in applying the genetic algorithm is to represent each decision variable of the simulation stimulus as string-structured data by encoding. The most common binary coding scheme, i.e. a string of symbols made of binary numbers, is used to represent an individual simulation stimulus. Firstly, determining the length of a binary string representing each decision variable according to the lower bound and the upper bound of each decision variable and the searching precision thereof, thereby realizing coding, and then randomly generating an initial simulation excitation population with a specified size.
The encoded population formed by the simulation excitation individuals must be decoded to be converted into the population formed by the original simulation excitation, and corresponding adaptive values are obtained through simulation and calculation. The key of the decoding function is to obtain the corresponding decimal number from the binary number, and then obtain the actual decision variable value.
Genetic manipulation design-selection, crossover and variation
In the selection process, the sizes of the adaptive values of the simulated excitation individuals obtained after decoding are utilized, some poor individuals are eliminated, and some excellent individuals are selected for the next step of crossing and mutation operation. Firstly, the simulation excitation individuals with the highest adaptation value and the lowest adaptation value in the current population are found, the best individual is reserved, and the worst individual is replaced by the best individual. In order to ensure that the current best individual is not damaged by crossover and mutation operations, the best individual is allowed to directly enter the next generation without participating in crossover and mutation. The remaining simulated excitation is then scaled to the magnitude of the adapted value. The crossover operator can be realized by a single-point crossover method, namely, a crossover point is randomly arranged in the simulation excitation individual code strings paired in pairs according to the selection probability, and then partial genes of the two paired individuals are mutually exchanged at the crossover point, so that two new simulation excitations are formed. For binary gene strings, mutation points are randomly selected according to mutation probability, and the positions of the mutation points are inverted.
Decoding, selection, interleaving and mutation are repeatedly performed until a convergence criterion is satisfied.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
According to the simulation method for the performance evaluation system, provided by the embodiment of the invention, evaluation requirements for the performance evaluation system to be evaluated are obtained, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining a simulation model corresponding to the multiple acquisition points according to the parameter information of the multiple acquisition points, so that the simulation model can simulate at the acquisition points to obtain a simulation result; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to the comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
Another embodiment of the present invention provides a simulation apparatus for a performance evaluation system, which is used to execute the simulation method for the performance evaluation system provided in the above embodiments.
Referring to fig. 5, a block diagram of an embodiment of a simulation apparatus for a performance evaluation system according to the present invention is shown, and the apparatus may specifically include the following modules: an obtaining module 501, a determining module 502, a simulating module 503 and a receiving module 504, wherein:
the obtaining module 501 is configured to obtain evaluation requirements for a performance evaluation system to be evaluated, where the evaluation requirements at least include an evaluation task, an evaluation condition, and an evaluation target to be achieved;
the determining module 502 is configured to determine a simulation acquisition order according to the evaluation requirement, where the simulation acquisition order at least includes parameter information of a plurality of acquisition points;
the simulation module 503 is configured to determine, according to the parameter information of the multiple acquisition points, a simulation model corresponding to the multiple acquisition points, so that the simulation model performs simulation at the acquisition points to obtain a simulation result;
the receiving module 504 is configured to receive a simulation result returned by the simulation model, compare the simulation result with the evaluation requirement, and perform adaptive adjustment on the simulation model according to the comparison result.
The simulation device for the performance evaluation system provided by the embodiment of the invention obtains the evaluation requirements to be evaluated for the performance evaluation system, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining simulation models corresponding to the plurality of acquisition points according to the parameter information of the plurality of acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to a comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
The present invention further provides a simulation apparatus for a performance evaluation system provided in the above embodiments.
Optionally, the determining module is configured to:
acquiring a top-level index in an evaluation target;
gradually unfolding and decomposing the top-layer indexes into lower-layer indexes until the lower-layer indexes carry characteristic information, wherein the lower-layer indexes are used for calculating on a simulation model;
and determining the parameter information of each acquisition point on the simulation acquisition list according to the lower-layer index.
Optionally, the receiving module is configured to:
receiving a simulation result obtained after a simulation model carries out data acquisition, storage and access operations according to parameter information of a simulation acquisition list on the acquisition list;
and performing measurement analysis on the evaluation index according to the simulation result to obtain an evaluation result.
Optionally, the receiving module is configured to:
comparing the simulation result with the evaluation result to obtain a comparison result;
and if the comparison result does not meet the evaluation requirement, adjusting the simulation excitation.
Optionally, the receiving module is specifically configured to:
respectively adopting one or more of the following comparison modes for comparison to obtain comparison results;
wherein the comparison result at least comprises the comparison of the precision of the simulation result and the comparison of the simulation result with the optimal solution.
Optionally, the receiving module is configured to:
adopting a genetic algorithm to adjust the self-adaptive simulation excitation to obtain an adjustment result;
and performing self-adaptive adjustment on each simulation model according to the adjustment result.
Optionally, the format of the simulation acquisition order is a data format convenient for editing and understanding, and the simulation acquisition order at least comprises a mapping relation between each index and the acquisition point.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The simulation device for the performance evaluation system provided by the embodiment of the invention obtains the evaluation requirements to be evaluated for the performance evaluation system, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining simulation models corresponding to the plurality of acquisition points according to the parameter information of the plurality of acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to the comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
Still another embodiment of the present invention provides a terminal device, configured to execute the simulation method for a performance evaluation system provided in the foregoing embodiment.
Fig. 6 is a schematic structural diagram of a terminal device according to the present invention, and as shown in fig. 6, the terminal device includes: at least one processor 601 and memory 602;
the memory stores a computer program; the at least one processor executes the computer program stored in the memory to implement the simulation method for the performance evaluation system provided by the above embodiments.
The terminal device provided by this embodiment obtains an evaluation requirement to be evaluated, which is oriented to a performance evaluation system, where the evaluation requirement at least includes an evaluation task, an evaluation condition, and an evaluation target to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining a simulation model corresponding to the multiple acquisition points according to the parameter information of the multiple acquisition points, so that the simulation model can simulate at the acquisition points to obtain a simulation result; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to the comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
Yet another embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed, the simulation method oriented to the performance evaluation system provided in any of the above embodiments is implemented.
According to the computer-readable storage medium of the embodiment, evaluation requirements facing the performance evaluation system to be evaluated are obtained, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved; determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points; determining simulation models corresponding to the plurality of acquisition points according to the parameter information of the plurality of acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results; receiving a simulation result returned by the simulation model, comparing the simulation result with an evaluation requirement, performing self-adaptive adjustment on the simulation model according to a comparison result, establishing the simulation model with dynamic adjustment behavior description capability, supporting a simulation data acquisition technology of self-adaptive adjustment acquisition points and a self-adaptive construction technology of simulation excitation, continuously improving an output result, and further realizing optimization of the performance of the simulation system.
It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than those illustrated or otherwise described herein.
Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Spatially relative terms, such as "above … …", "above … …", "above … …, on a surface", "above", and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may also be oriented in other different ways, such as by rotating it 90 degrees or at other orientations, and the spatially relative descriptors used herein interpreted accordingly.
In the above detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like numerals typically identify like components, unless context dictates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A simulation method for a performance evaluation system, the method comprising:
acquiring evaluation requirements to be evaluated, which are oriented to a performance evaluation system, wherein the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved;
determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points;
determining simulation models corresponding to the acquisition points according to the parameter information of the acquisition points, so that the simulation models can simulate at the acquisition points to obtain simulation results;
and receiving a simulation result returned by the simulation model, comparing the simulation result with the evaluation requirement, and performing self-adaptive adjustment on the simulation model according to the comparison result.
2. The method of claim 1, wherein determining a simulation acquisition order based on the evaluation requirements comprises:
acquiring a top-level index in an evaluation target;
gradually unfolding and decomposing the top layer indexes into lower layer indexes until the lower layer indexes carry characteristic information, wherein the lower layer indexes are used for calculation on a simulation model;
and determining the parameter information of each acquisition point on the simulation acquisition list according to the lower-layer index.
3. The method of claim 2, wherein receiving the simulation results returned by the simulation model comprises:
receiving a simulation result obtained after the simulation model carries out data acquisition, storage and access operations according to the parameter information of the simulation acquisition list on the acquisition list;
and performing measurement analysis on the evaluation index according to the simulation result to obtain an evaluation result.
4. The method of claim 3, further comprising:
comparing the simulation result with the evaluation result to obtain a comparison result;
and if the comparison result does not meet the evaluation requirement, adjusting the simulation excitation.
5. The method of claim 1, wherein comparing the simulation result and the evaluation result to obtain a comparison result comprises:
respectively adopting one or more of the following comparison modes for comparison to obtain comparison results;
wherein the comparison result at least comprises the comparison of the precision of the simulation result and the comparison of the simulation result with the optimal solution.
6. The method of claim 5, wherein the adaptively adjusting the simulation model according to the comparison result comprises:
adopting a genetic algorithm to adjust the self-adaptive simulation excitation to obtain an adjustment result;
and performing self-adaptive adjustment on each simulation model according to the adjustment result.
7. The method according to claim 1, wherein the format of the simulation acquisition order is a data format convenient for editing and understanding, and the simulation acquisition order at least comprises a mapping relation between each index and an acquisition point.
8. A simulation apparatus for a performance evaluation system, the apparatus comprising:
the system comprises an acquisition module, a performance evaluation module and a performance evaluation module, wherein the acquisition module is used for acquiring evaluation requirements to be evaluated, which are oriented to a performance evaluation system, and the evaluation requirements at least comprise evaluation tasks, evaluation conditions and evaluation targets to be achieved;
the determining module is used for determining a simulation acquisition list according to the evaluation requirement, wherein the simulation acquisition list at least comprises parameter information of a plurality of acquisition points;
the simulation module is used for determining simulation models corresponding to the acquisition points according to the parameter information of the acquisition points so as to enable the simulation models to simulate at the acquisition points to obtain a simulation result;
and the receiving module is used for receiving the simulation result returned by the simulation model, comparing the simulation result with the evaluation requirement and carrying out self-adaptive adjustment on the simulation model according to the comparison result.
9. A terminal device, comprising: at least one processor and memory;
the memory stores a computer program; the at least one processor executes the computer program stored in the memory to implement the performance evaluation system oriented simulation method of any of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored, which, when executed, implements the performance evaluation system-oriented simulation method of any one of claims 1 to 7.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060294436A1 (en) * | 2004-03-10 | 2006-12-28 | Fujitsu Limited | Appararus for predicting reliability in electronic device package, program for predicting reliability in electronic device package, and method for predicting reliability in electronic device package |
| CN106951576A (en) * | 2016-01-06 | 2017-07-14 | 北京仿真中心 | A kind of l-G simulation test assessment system and appraisal procedure |
| CN109300354A (en) * | 2018-09-30 | 2019-02-01 | 上海机电工程研究所 | A kind of Weapon Equipment System Counter Simulation System |
| CN109614729A (en) * | 2018-12-18 | 2019-04-12 | 上海机电工程研究所 | A kind of equipment Efficacy assessment result rapid analysis method based on meta-model |
| CN111144025A (en) * | 2019-12-31 | 2020-05-12 | 中国兵器装备集团自动化研究所 | Simulation model parameterization integration system and method based on multi-level reconstruction |
| CN111274727A (en) * | 2020-01-17 | 2020-06-12 | 南京星火技术有限公司 | Electronic device, microwave product model designing apparatus, and computer-readable medium |
| CN111667149A (en) * | 2020-05-13 | 2020-09-15 | 北京理工大学 | System efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation |
| CN112052608A (en) * | 2020-10-16 | 2020-12-08 | 中国直升机设计研究所 | Integrated method for war deduction simulation and efficiency evaluation |
| CN112883527A (en) * | 2021-03-16 | 2021-06-01 | 中国人民解放军国防科技大学 | Simulation evaluation method for network system |
| CN114265309A (en) * | 2021-11-19 | 2022-04-01 | 江苏徐工工程机械研究院有限公司 | Engineering machinery optimization control strategy recommendation system and method |
| CN114757051A (en) * | 2022-05-05 | 2022-07-15 | 西安亨孚防务科技有限责任公司 | Method and device for verifying confrontation function level simulation of air-defense missile weapon system |
-
2022
- 2022-08-30 CN CN202211059331.3A patent/CN115130332A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060294436A1 (en) * | 2004-03-10 | 2006-12-28 | Fujitsu Limited | Appararus for predicting reliability in electronic device package, program for predicting reliability in electronic device package, and method for predicting reliability in electronic device package |
| CN106951576A (en) * | 2016-01-06 | 2017-07-14 | 北京仿真中心 | A kind of l-G simulation test assessment system and appraisal procedure |
| CN109300354A (en) * | 2018-09-30 | 2019-02-01 | 上海机电工程研究所 | A kind of Weapon Equipment System Counter Simulation System |
| CN109614729A (en) * | 2018-12-18 | 2019-04-12 | 上海机电工程研究所 | A kind of equipment Efficacy assessment result rapid analysis method based on meta-model |
| CN111144025A (en) * | 2019-12-31 | 2020-05-12 | 中国兵器装备集团自动化研究所 | Simulation model parameterization integration system and method based on multi-level reconstruction |
| CN111274727A (en) * | 2020-01-17 | 2020-06-12 | 南京星火技术有限公司 | Electronic device, microwave product model designing apparatus, and computer-readable medium |
| CN111667149A (en) * | 2020-05-13 | 2020-09-15 | 北京理工大学 | System efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation |
| CN112052608A (en) * | 2020-10-16 | 2020-12-08 | 中国直升机设计研究所 | Integrated method for war deduction simulation and efficiency evaluation |
| CN112883527A (en) * | 2021-03-16 | 2021-06-01 | 中国人民解放军国防科技大学 | Simulation evaluation method for network system |
| CN114265309A (en) * | 2021-11-19 | 2022-04-01 | 江苏徐工工程机械研究院有限公司 | Engineering machinery optimization control strategy recommendation system and method |
| CN114757051A (en) * | 2022-05-05 | 2022-07-15 | 西安亨孚防务科技有限责任公司 | Method and device for verifying confrontation function level simulation of air-defense missile weapon system |
Non-Patent Citations (4)
| Title |
|---|
| 王国新等: "《离散事件系统仿真原理与AutoMod的应用》", 31 March 2011, 原子能出版社 * |
| 黄炎焱: "《系统建模仿真技术与应用》", 31 December 2016, 国防工业出版社 * |
| 黄炎焱等: "仿真数据综合可信度的测度与校验方法研究", 《系统工程学报》 * |
| 黄炎焱等: "基于仿真数据采集单的仿真评估需求研究", 《系统仿真学报》 * |
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