CN111667149A - System efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation - Google Patents
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Abstract
The invention provides a system efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation, which comprises the following steps: configuring a simulation model library of a large-scale scientific and technological engineering system, configuring management network parameters and research network parameters, and then configuring resource allocation rules according to the management network parameters and the research network parameters; configuring an operation state and an expert evaluation data source; configuring an experiment task; running an experiment; and analyzing a simulation result. The invention establishes a method which is oriented to large-scale scientific and technical engineering and can bring actual service information and static evaluation data into a simulation model for fusion calculation; and the simulation model is corrected in real time by using the annual expert evaluation data, so that the model has stronger prediction capability.
Description
Technical Field
The invention relates to the technical field of efficiency evaluation, in particular to a system efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation.
Background
(1) The existing efficiency evaluation method is mainly oriented to a single system, generally, the system is divided into a plurality of sub-modules, and then evaluation is respectively carried out and results are summarized; the large-scale scientific and technological engineering is characterized by comprising a plurality of subsystems, wherein nodes in the subsystems are mutually associated to form a complex network, and in the whole large-scale scientific and technological engineering system, although the large-scale scientific and technological engineering system has a certain upper-level and lower-level hierarchical structure, the operation evolution of the system depends on the interaction of the nodes in the complex network to a greater extent rather than the top-down administrative command. Therefore, the conventional performance evaluation method cannot be applied to the evolution analysis and system performance evaluation work of large-scale scientific and technological engineering.
(2) In the current practice, a method based on expert scoring is mainly used for evaluating the system efficiency of large-scale scientific and technological engineering, the method highly depends on the construction of an evaluation system and evaluation indexes, the latter also needs to be designed by experts, and the evaluation system is long in construction time, high in cost, difficult to adjust and multiple in human influence factors.
(3) At present, in advanced research in this field, an evaluation method based on a simulation model is mainly used, the method needs to rely on a large number of hypothesis conditions, and under the condition that the actual research data of the large-scale scientific and technological engineering system is insufficient, the model hypotheses generally rely on ideal hypotheses, so that the actual operation data of the simulation model and the actual large-scale scientific and technological engineering system are not closely butted, and the reference and guidance of the operation result of the simulation model to the actual scientific and technological engineering management work are insufficient.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide a system efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation.
In order to achieve the above object, an embodiment of the present invention provides a system performance evaluation method for large-scale scientific and technological engineering, simulation, and expert evaluation, including the following steps:
step S1, according to the calculation work selection model, configuring management network parameters and research network parameters, and then according to the management network parameters and the research network parameters, configuring resource allocation rules;
step S2, configuring an operation state and an expert evaluation data source;
step S3, configuring an experiment task;
step S4, running an experiment;
step S5, the simulation result is analyzed.
Further, the simulation model library of the large-scale scientific and technological engineering system comprises: an overall evolution model of a large-scale scientific and technological engineering system and a static evaluation model of the large-scale scientific and technological engineering system, wherein,
the integral evolution model of the large-scale scientific and technological engineering system comprises the following steps: the method comprises the following steps of (1) a large-scale scientific and technological engineering research network evolution model, a large-scale scientific and technological engineering management network evolution model and a large-scale scientific and technological engineering data correction model;
the large-scale scientific and technological engineering system static evaluation model comprises the following steps: a large-scale scientific and technological engineering management capability evaluation model and a large-scale scientific and technological engineering research capability evaluation model.
Further, in the step S3, configuring an experiment task includes: and setting a multi-time operation mode, a parameter value or parameter generation rule, experiment operation times and stop conditions of the simulation model library of the large-scale scientific and technological engineering system.
Further, in the step S4, the running the experiment includes: and calling a simulation model to generate and store a calculation result according to the experimental configuration.
Further, in the step S5, the analyzing the simulation result includes: and selecting a performance analysis template of a large-scale scientific and technological engineering system, and analyzing the simulation experiment result.
Further, the analyzing the simulation experiment result includes: and carrying out overall analysis, research network analysis, management network analysis, annual analysis and multi-situation analysis on simulation experiment results.
Further, setting a value interval and a value interval for the determined model parameters, generating a value of each parameter, and performing orthogonality on all the parameter values to generate N pieces of experimental point data;
calculating the capability score simulation effect of the T year by using a simulation model, calculating the error between the simulation result and the capability score obtained based on the expert scoring table, and obtaining the minimum error value;
if the minimum error value does not reach the preset target, carrying out the next round of experiment, reselecting the parameter value interval and the value interval, and recalculating the error until the error target is reached;
and selecting a group of experimental parameters with the minimum error as model prediction parameters.
Further, configuring the management data includes: a scientific and technological engineering overall research plan; the name, function and team ability evaluation scores of the management department; planning management, plan management and contract management approval processes of large-scale scientific and technological engineering; research field, research discipline, capability index data of the research team.
Further, the network evolution model for large-scale scientific and technical engineering research is realized by simulating the complex network evolution law formed by the research team.
And further, calculating a capability score simulation result of the T year by using a simulation model, calculating an error between the simulation result and the capability score obtained based on an expert scoring table, calculating a minimum error value, if the minimum error value does not reach a target, performing the next round of experiment, reselecting a parameter value interval and a value interval, and recalculating the error until the error target is reached.
Further, configuring a simulation model of a large-scale scientific and technical engineering system, comprising: and configuring a model input data source and an output data format.
According to the system efficiency evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation of the embodiment of the invention,
(1) a method facing large-scale scientific and technical engineering and capable of bringing actual service information and static evaluation data into a simulation model for fusion calculation is established;
(2) the simulation model is corrected in real time by using the annual expert evaluation data, so that the model has stronger prediction capability;
(3) in the simulation model, according to the practical characteristics of large-scale scientific and technical engineering, two complex system functions of a management network and a research network are established, the complexity of model assumption is reduced, and the interpretability of the operation result of the simulation model is improved.
(4) The method comprises a simulation model library, an experiment configuration function, an experiment operation environment and a data storage function which are oriented to the overall evolution and static evaluation of a large-scale scientific and technological engineering system.
(5) Research network, management network and data correction function in the overall evolution model of the large-scale scientific and technological engineering system.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a block diagram of a system performance evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation according to an embodiment of the present invention;
FIG. 2 is a flowchart of a system performance evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of static evaluation and dynamic evolution according to an embodiment of the invention;
fig. 4 is an architecture diagram of a system performance evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1, the system performance evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation according to the embodiment of the present invention includes the following steps:
step S1, configuring a simulation model base of the large-scale scientific and technical engineering system, configuring management network parameters and research network parameters, and then configuring resource allocation rules according to the management network parameters and the research network parameters.
Specifically, configuring a simulation model of a large-scale scientific and technical engineering system comprises the following steps: one item of static evaluation, model training and model prediction is selected, and a model input data source and an output data format are configured.
(1) Configuring an input data file:
(2) configuring model operating parameters
TABLE 1
TABLE 2
In an embodiment of the present invention, the simulation model library of the large-scale scientific and technical engineering system includes: an overall evolution model of a large-scale scientific and technological engineering system and a static evaluation model of the large-scale scientific and technological engineering system, wherein,
the large-scale scientific and technical engineering system integral evolution model comprises: the method comprises the following steps of (1) a large-scale scientific and technological engineering research network evolution model, a large-scale scientific and technological engineering management network evolution model and a large-scale scientific and technological engineering data correction model;
the large-scale scientific and technical engineering system static evaluation model comprises the following steps: a large-scale scientific and technological engineering management capability evaluation model and a large-scale scientific and technological engineering research capability evaluation model.
In this step, configuring the management data includes: a scientific and technological engineering overall research plan; the name, function and team ability evaluation scores of the management department; planning management, plan management and contract management approval processes of large-scale scientific and technological engineering; research field, research discipline, capability index data of the research team.
In the invention, the simulation model library for the large-scale scientific and technological engineering system comprises: and (5) an integral evolution model of a large-scale scientific and technological engineering system.
The following data were imported: a scientific and technological engineering overall research plan; the name, function and team ability evaluation scores of the management department; planning management, plan management and contract management approval processes of large-scale scientific and technological engineering; research field, research subject, and ability index data (number of issued papers, number of patents, number of awards, number of team persons (number of senior job title, middle job title, and primary job title)).
The invention can realize the following functions:
predicting the number of contracts, contract execution period, contract attribution team and contract score every year in the future based on a research plan; predicting the capacity of a research team and the capacity of a management department; calculating the efficiency of a scientific and technological engineering system; a large-scale scientific and technical engineering research network evolution model simulates and realizes the complex network evolution law formed by a research team.
The invention can realize the simulation of the following aspects:
predicting cross-team partnerships (if at least one member belongs to two teams at the same time, the two teams are called to have cross-team partnerships); structural evolution (network scale, centrality, vulnerability) of a research collaboration network based on cross-team collaboration; the large-scale scientific and technical engineering management network evolution model is used for simulating the service flow of a management system; predicting the quantity and efficiency of examination and approval contracts of each management department every year in the future; and predicting the change of the management business process of each management department every year in the future.
The invention further provides a static evaluation module of the large-scale scientific and technological engineering, which comprises management capability evaluation and research capability evaluation, and can derive a static evaluation result at any evolution time of the simulation model, and refer to table 3.
TABLE 3
And step S2, configuring an operation state and expert evaluation data source.
And configuring the running state data and the efficiency evaluation data of the large-scale scientific and technological engineering system, and establishing the incidence relation between the annual data and the simulation model.
According to the level, the actual meaning and the data format characteristics of the static evaluation indexes and the variable meaning, the level and other factors of the dynamic evolution model, the following strategies are used for carrying out the corresponding of the indexes:
the final-stage index (team ability score) of the talent team ability evaluation corresponds to the ability value of the team agent of the dynamic evolution model, and the top-stage index (primary and secondary indexes) of the management ability evaluation index corresponds to each global parameter (namely system macroscopic hypothesis) of the dynamic evolution model. The specific correspondence is shown in table 4.
TABLE 4
Step S3, configure the experiment task.
In this step, configuring the experimental task includes: setting multiple operation modes (Monte Carlo, multi-situation, genetic algorithm and the like) of a simulation model library of a large-scale scientific and technological engineering system, parameter values or parameter generation rules, experiment operation times, stopping conditions and the like.
Step S4, run the experiment.
In this step, the run experiment included: and calling a simulation model to generate and store a calculation result according to the experimental configuration.
In an embodiment of the invention, the simulation experiment operating environment is: the hosting operation environment of the simulation model can carry out single operation, multiple operation or multiple iterative operation of adjusting parameters according to the previous operation result on the specified simulation model according to the experimental design.
Step S5, the simulation result is analyzed.
In this step, analyzing the simulation result includes: and selecting a performance analysis template of a large-scale scientific and technological engineering system, and analyzing the simulation experiment result.
Analyzing the simulation experiment result, comprising: and carrying out overall analysis, research network analysis, management network analysis, annual analysis and multi-situation analysis on simulation experiment results.
The invention provides a visual analysis function of an operation result: and extracting and carrying out multi-dimensional cross analysis on the simulation model operation result data, and presenting the result in visualization modes such as a line chart, a pie chart, a scatter diagram and the like.
In addition, the invention can provide a large-scale scientific and technological engineering data correction function, input expert evaluation data into the model and train model parameters. See attachment table 3 expert scoring table. The training method comprises the following steps:
(1) setting value intervals and value intervals for model parameters to be determined (mainly comprising a team capacity growth hypothesis, a team cooperation tendency hypothesis, a management department capacity growth hypothesis and a management process change hypothesis), generating values of each parameter, carrying out orthogonality on the values of all the parameters, and generating N experiments (N is m1 m2 m3 …, m1, m2 and m3 are points of each parameter)
(2) Calculating the capability score simulation result of the T year by using the simulation model, calculating the error between the simulation result and the capability score obtained based on the expert scoring table, and obtaining the minimum error value
(3) If the minimum error value does not reach the target, carrying out the next round of experiment, reselecting the parameter value interval and the value interval, and recalculating the error until the error target is reached
(4) And taking a group of experimental parameters with the minimum error as model prediction parameters.
The system efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation of the embodiment of the invention further comprises the following steps: and (3) simulating and realizing the complex network evolution rule formed by the research team on the large-scale scientific and technical engineering research network evolution model.
And (3) calculating a capability score simulation result of the T year by using a simulation model, calculating an error between the simulation result and the capability score obtained based on the expert scoring table, calculating a minimum error value, if the minimum error value does not reach a target, performing the next round of experiment, reselecting a parameter value interval and a parameter value interval, and recalculating the error until the error target is reached.
According to the system efficiency evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation of the embodiment of the invention,
(1) a method facing large-scale scientific and technical engineering and capable of bringing actual service information and static evaluation data into a simulation model for fusion calculation is established;
(2) the simulation model is corrected in real time by using the annual expert evaluation data, so that the model has stronger prediction capability;
(3) in the simulation model, according to the practical characteristics of large-scale scientific and technical engineering, two complex system functions of a management network and a research network are established, the complexity of model assumption is reduced, and the interpretability of the operation result of the simulation model is improved.
(4) The method comprises a simulation model library, an experiment configuration function, an experiment operation environment and a data storage function which are oriented to the overall evolution and static evaluation of a large-scale scientific and technological engineering system.
(5) Research network, management network and data correction function in the overall evolution model of the large-scale scientific and technological engineering system.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A system efficiency evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation is characterized by comprising the following steps:
step S1, configuring a simulation model library of a large-scale scientific and technical engineering system, configuring management network parameters and research network parameters, and then configuring resource allocation rules according to the management network parameters and the research network parameters;
step S2, configuring an operation state and an expert evaluation data source;
step S3, configuring an experiment task;
step S4, running an experiment;
step S5, the simulation result is analyzed.
2. The performance evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation system as claimed in claim 1, wherein the simulation model library of the large-scale scientific and technological engineering system comprises: an overall evolution model of a large-scale scientific and technological engineering system and a static evaluation model of the large-scale scientific and technological engineering system, wherein,
the integral evolution model of the large-scale scientific and technological engineering system comprises the following steps: the method comprises the following steps of (1) a large-scale scientific and technological engineering research network evolution model, a large-scale scientific and technological engineering management network evolution model and a large-scale scientific and technological engineering data correction model;
the large-scale scientific and technological engineering system static evaluation model comprises the following steps: a large-scale scientific and technological engineering management capability evaluation model and a large-scale scientific and technological engineering research capability evaluation model.
3. The performance evaluation method for systems oriented to large-scale scientific and technical engineering, simulation and expert evaluation as claimed in claim 1, wherein in the step S3, configuring the experimental task comprises: setting a multi-time operation mode, a parameter value or parameter generation rule, experiment operation times and stop conditions of a simulation model library of the large-scale scientific and technological engineering system;
in the step S4, the running experiment includes: and calling a simulation model to generate and store a calculation result according to the experimental configuration.
4. The performance evaluation method for systems oriented to large-scale scientific and technical engineering, simulation and expert evaluation as claimed in claim 1, wherein in the step S5, the analyzing the simulation result comprises: and selecting a performance analysis template of a large-scale scientific and technological engineering system, and analyzing the simulation experiment result.
5. The performance evaluation method for systems of large-scale scientific and technical engineering, simulation and expert evaluation as claimed in claim 4, wherein the analyzing the simulation experiment results comprises: and carrying out overall analysis, research network analysis, management network analysis, annual analysis and multi-situation analysis on simulation experiment results.
6. The performance evaluation method for large-scale scientific and technical engineering, simulation and expert evaluation system as claimed in claim 1, wherein a value interval and a value interval are set for the determined model parameters, a value of each parameter is generated, and all parameter values are orthogonal to generate N pieces of experimental point data;
calculating the capability score simulation effect of the T year by using a simulation model, calculating the error between the simulation result and the capability score obtained based on the expert scoring table, and obtaining the minimum error value;
if the minimum error value does not reach the preset target, carrying out the next round of experiment, reselecting the parameter value interval and the value interval, and recalculating the error until the error target is reached;
and selecting a group of experimental parameters with the minimum error as model prediction parameters.
7. The performance evaluation method for systems oriented to large-scale scientific engineering, simulation and expert evaluation as claimed in claim 1, wherein configuring the management data comprises: a scientific and technological engineering overall research plan; the name, function and team ability evaluation scores of the management department; planning management, plan management and contract management approval processes of large-scale scientific and technological engineering; research field, research discipline, capability index data of the research team.
8. The performance evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation system as claimed in claim 1, wherein the network evolution model of large-scale scientific and technological engineering research is realized by simulating the complex network evolution law formed by research teams.
9. The performance evaluation method for the system of large-scale scientific and technical engineering, simulation and expert evaluation as claimed in claim 1, wherein the simulation model is used to calculate the capability score simulation result of the T year, calculate the error between the simulation result and the capability score obtained based on the expert scoring table, calculate the minimum error value, if the minimum error value does not reach the target, perform the next round of experiment, reselect the parameter value interval and the value interval, and recalculate the error until the error target is reached.
10. The system performance evaluation method for large-scale scientific and technological engineering, simulation and expert evaluation as claimed in claim 1, wherein configuring the large-scale scientific and technological engineering system simulation model comprises: and configuring a model input data source and an output data format.
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| CN107967134A (en) * | 2017-11-20 | 2018-04-27 | 中国人民解放军国防科技大学 | Novel combat effectiveness simulation modeling method |
| 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 |
| CN109409695A (en) * | 2018-09-30 | 2019-03-01 | 上海机电工程研究所 | System Effectiveness evaluation index system construction method and system based on association analysis |
| CN109948228A (en) * | 2019-02-27 | 2019-06-28 | 中国舰船研究设计中心 | A kind of confronting simulation and Effectiveness Evaluation System based on equipment parametrization |
| CN110188385A (en) * | 2019-04-26 | 2019-08-30 | 中国电力科学研究院有限公司 | A kind of method and system for electric system simulation scene modeling |
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| CN115130332A (en) * | 2022-08-30 | 2022-09-30 | 中电太极(集团)有限公司 | Simulation method and device for efficiency evaluation system |
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