Modelica model confidence evaluation system and method thereof

Abstract

The invention provides a Modelica model confidence evaluation system and a method thereof, and relates to the field of equipment digital Modelica simulation model checking and verification. The Modelica model confidence evaluation system comprises an index decomposition function, an evaluation algorithm selection function, a weight distribution function, an evaluation result calculation function, a credibility statistics function and a credibility analysis tool function, and the index decomposition function can customize a simulation system structure of a model and decompose a simulation system into subsystems and subset hierarchies thereof. And completing the decomposition of the model indexes. By comprehensively considering the diversity of a Modelica simulation model and different model checking and verification methods, a unified confidence evaluation system and method from model index decomposition to credibility analysis are formed, stylization and interface of the whole model confidence evaluation process are realized, tedious steps of the evaluation process are simplified, and the evaluation efficiency is improved. Therefore, time and energy of workers are saved.

Description

Technical Field

[0001] The present invention relates to the field of equipment digitalization Modelica simulation model checking and verification, and in particular to a Modelica model confidence evaluation system and method thereof. Background Art

[0002] Modelica is an open, object-oriented, equation-based computer language that can cross different fields and easily realize the modeling of complex physical systems, including mechanical, electronic, electrical, hydraulic, thermal, control and process-oriented subsystem models. With the development of simulation technology and the advancement of equipment digitization, the complexity of digital system models is becoming higher and higher. The model confidence assessment of simulation systems is very important for the promotion of digital models.

[0003] There are many different methods for checking and verifying digital models, but there is no unified description and full-process system method for checking and verifying digital models. For a simulation model, especially a simulation model of a complex system, if the simulation confidence assessment relies on manual or semi-manual implementation, it will take a lot of time and it is easy for human negligence to cause deviations in the assessment results. Therefore, it is very important and urgent to form a unified model confidence assessment method and its application system in terms of model confidence assessment. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides a Modelica model confidence assessment system and method thereof. The present invention provides a Modelica model confidence assessment system and method thereof based on a Modelica model. By comprehensively considering the diversity of Modelica simulation models and different model verification and validation methods, a unified confidence assessment system and method from model indicator decomposition to credibility analysis is formed. The process method mainly includes indicator decomposition, selection of evaluation algorithm, weight allocation, evaluation result calculation, credibility statistics, and credibility analysis parts, and has built-in indicator decomposition tools, multiple evaluation algorithm selection tools, weight allocation tools, evaluation result calculation tools, credibility statistics tools, and credibility analysis tools, and supports visualization of the results of the entire evaluation process, and the operation is simple and fast.

[0006] (II) Technical solution

[0007] To achieve the above purpose, the present invention is implemented through the following technical solutions: a Modelica model confidence evaluation system and method thereof, the Modelica model confidence evaluation system includes an indicator decomposition function, an evaluation algorithm selection function, a weight allocation function, an evaluation result calculation function, a credibility statistics function and a credibility analysis tool function, the indicator decomposition function can customize the simulation system structure of the model, decompose the simulation system into subsystems and their subset levels, and complete the decomposition of the model indicators, the evaluation algorithm selection function clicks the selection button to select the file path of the experiment and simulation data for each indicator to be decomposed, and then selects the evaluation algorithm, and different built-in evaluation algorithms can be selected, including TIC (time inconsistency coefficient), average Euclidean distance coefficient, correlation coefficient, angular cosine coefficient, steady-state maximum error, periodic spectrum analysis, the weight allocation function Different indicators can be displayed in a table according to the simulation system structure, and custom weights and automatic weight allocation are supported, involving expert scoring method and rank ratio method for weight allocation respectively. The evaluation result calculation calculates the confidence of the model according to the results of the indicator decomposition, selection of evaluation algorithm and weight allocation in the previous step, and the expected thresholds of different decomposition sub-elements can be set to facilitate the display of expected results of feasibility statistics. The credibility statistics displays the results of the evaluation result calculation in pie charts, bar charts and tables, and displays different colors according to the set expected thresholds. The system defaults to green, yellow and red to represent satisfactory, basically satisfactory and unsatisfactory confidence evaluation results respectively. The credibility analysis tool performs confidence analysis on the evaluation results, and has built-in multiple different analysis algorithms including minimum confidence analysis, maximum weight analysis and maximum optimization algorithm. It gives the optimization results of different algorithms and gives suggestions for model improvement.

[0008] Preferably, the evaluation system supports the functions of adding sub-items, deleting, selecting evaluation algorithms, and assigning weights in the right-click menu of the root node element.

[0009] Preferably, the evaluation system supports configuration of basic attributes, expected values, and attention items in the node attribute column.

[0010] Preferably, the evaluation system supports the functions of adding sub-items, deleting, displaying curves, selecting evaluation algorithms, and assigning weights in the right-click menu of the intermediate node elements.

[0011] Preferably, the evaluation system supports the functions of adding sub-items, deleting, and displaying curves in the right-click menu of the terminal node.

[0012] (III) Beneficial effects

[0013] The present invention provides a Modelica model confidence evaluation system and method thereof, which has the following beneficial effects:

[0014] 1. The present invention provides a Modelica model confidence assessment system and method thereof. The model confidence assessment method of the system abstracts the entire process into six parts that are easy to understand and operate, including indicator decomposition, selection of evaluation algorithm, allocation of weights, evaluation result calculation, credibility statistics, and credibility analysis, forming a unified confidence assessment indicator system.

[0015] 2. The present invention provides a Modelica model confidence assessment system and method thereof, which realizes the programming and interface of the whole process of model confidence assessment, simplifies the cumbersome steps of the assessment process, and thus saves the time and energy of the staff.

[0016] 3. The present invention provides a Modelica model confidence assessment system and method, which supports the decomposition of model indicators in the assessment process, the name, depth, weight, assessment algorithm, expected value attribute setting of the decomposition node, the visualization display of the confidence calculation results and the generation of confidence reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A model index decomposition diagram of a Modelica model confidence evaluation system taking SSME as an example of the present invention;

[0018] Figure 2 A selection evaluation algorithm diagram of a Modelica model confidence evaluation system taking SSME as an example of the present invention;

[0019] Figure 3 A flow chart of custom weight allocation for the Modelica model confidence assessment system using SSME as an example of the present invention;

[0020] Figure 4 The automatic weight allocation flow chart of the Modelica model confidence evaluation system taking SSME as an example of the present invention;

[0021] Figure 5 Setting an indicator attribute graph for an attribute column of a Modelica model confidence evaluation system taking SSME as an example of the present invention;

[0022] Figure 6 It is an evaluation result calculation diagram of the Modelica model confidence evaluation system taking SSME as an example of the present invention;

[0023] Figure 7 A credibility statistics display diagram of the Modelica model confidence assessment system taking SSME as an example of the present invention;

[0024] Figure 8This is a credibility analysis display diagram of the Modelica model confidence assessment system taking SSME as an example of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of this application, it should be noted that the terms used here are only for describing specific implementations, and are not intended to limit the exemplary implementations according to the present application. For ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be regarded as part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0026] Embodiment 1:

[0027] like Figure 1-8As shown, an embodiment of the present invention provides a Modelica model confidence evaluation system and method thereof, the Modelica model confidence evaluation system includes an indicator decomposition function, an evaluation algorithm selection function, a weight allocation function, an evaluation result calculation function, a credibility statistics function and a credibility analysis tool function, the indicator decomposition function can customize the simulation system structure of the model, decompose the simulation system into subsystems and their subset levels, and complete the decomposition of the model indicators, select the evaluation algorithm function, click the selection button to select the file path of the experiment and simulation data for each indicator to be decomposed, and then select the evaluation algorithm. You can choose different built-in evaluation algorithms, including TIC (time inconsistency coefficient), average Euclidean distance coefficient, correlation coefficient, angular cosine coefficient, steady-state maximum error, periodic spectrum analysis, and the weight allocation function will be different indicators according to The simulation system structure is shown in the table. It supports custom weights and automatic weight allocation, involving expert scoring method and rank ratio method to allocate weights respectively. The evaluation result calculation calculates the confidence of the model according to the results of the indicator decomposition, selection of evaluation algorithm and weight allocation in the previous step, and the expected thresholds of different decomposition sub-elements can be set to facilitate the display of expected results of feasibility statistics. The credibility statistics displays the results of the evaluation result calculation in pie charts, bar charts and tables, and displays different colors according to the set expected thresholds. The system defaults to green, yellow and red to represent satisfactory, basically satisfactory and unsatisfactory confidence evaluation results respectively. The credibility analysis tool performs confidence analysis on the evaluation results. It has built-in multiple different analysis algorithms including minimum confidence analysis, maximum weight analysis and maximum optimization algorithm, gives the optimization results of different algorithms, and gives suggestions for model improvement.

[0028] The evaluation system supports the functions of adding sub-items, deleting, selecting evaluation algorithms, and assigning weights in the right-click menu of root node elements. The evaluation system supports the configuration of basic properties, expected values, and attention items in the node property bar. The evaluation system supports the functions of adding sub-items, deleting, displaying curves, selecting evaluation algorithms, and assigning weights in the right-click menu of intermediate node elements. The evaluation system supports the functions of adding sub-items, deleting, and displaying curves in the right-click menu of end nodes.

[0029] Embodiment 2:

[0030] like Figure 1-8 As shown, the embodiment of the present invention provides a Modelica model confidence assessment system and method thereof. To achieve the above-mentioned purpose, taking the confidence assessment of the Modelica model using SSME as an example, the embodiment of the present invention provides the following technical solutions:

[0031] Step 1: Decomposition of indicators

[0032] like Figure 1As shown, according to the SSME model, the simulation system is divided into indicators such as hydrogen supply system, oxygen supply system, and combustion system according to its internal structure and evaluation requirements.

[0033] Step 2: Select an evaluation algorithm

[0034] like Figure 2 As shown, select the file path for configuring the experimental data and simulation data, and select the TIC algorithm for the evaluation algorithm.

[0035] Step 3: Assign weights

[0036] like Figure 3 As shown, you can customize the weights of each indicator in the model, or automatically assign weights as shown in Figure 4 As shown, then Figure 5 As shown, the properties of the indicator node can be configured through the property bar, wherein the configuration of the expected value is the satisfaction threshold of the indicator confidence.

[0037] Step 4: Calculation of evaluation results

[0038] like Figure 6 As shown, the evaluation results are calculated, where green, yellow, and red represent satisfactory, basically satisfactory, and unsatisfactory confidence evaluation results.

[0039] Step 5: Credibility Statistics

[0040] like Figure 7 As shown in the figure, the confidence statistics will intuitively display the confidence assessment results in the form of a chart.

[0041] Step 6: Credibility Analysis Tool

[0042] like Figure 8 As shown in Figure 1, the credibility analysis tool provides a variety of confidence analysis methods and gives corresponding model improvement suggestions. For this example model, when the minimum confidence analysis method is selected, the results of the algorithm analysis are as follows: Figure 8 As shown, the confidence of the improved system increases from 0.838399 to 0.954325. The models that need to be improved include hydrogen turbine flow rate and hydrogen pre-combustion room temperature. Similarly, the maximum weight distribution and maximum limit can be selected for optimization analysis.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A Modelica model confidence assessment system, characterized by: The Modelica model confidence evaluation system includes indicator decomposition function, evaluation algorithm selection function, weight allocation function, evaluation result calculation function, credibility statistics function and credibility analysis tool function. The indicator decomposition function can customize the simulation system structure of the model, decompose the simulation system into subsystems and their subset levels, and complete the decomposition of model indicators. The evaluation algorithm selection function clicks the selection button to select the file path of the experiment and simulation data for each indicator to be decomposed, and then selects the evaluation algorithm. You can choose different built-in evaluation algorithms, including TIC (time inconsistency coefficient), average Euclidean distance coefficient, correlation coefficient, angular cosine coefficient, steady-state maximum error, and periodic spectrum analysis. The weight allocation function displays different indicators in the table according to the simulation system structure, and supports custom The defined weights and automatic weight allocation involve the expert scoring method and the rank ratio method for weight allocation respectively. The evaluation result calculation calculates the confidence of the model according to the results of the indicator decomposition, selection of the evaluation algorithm and weight allocation in the previous step, and the expected thresholds of different decomposition sub-elements can be set to facilitate the display of the expected results of the feasibility statistics. The credibility statistics displays the results of the evaluation result calculation in pie charts, bar charts and tables, and displays different colors according to the set expected thresholds. The system defaults to green, yellow and red to represent satisfactory, basically satisfactory and unsatisfactory confidence evaluation results respectively. The credibility analysis tool performs confidence analysis on the evaluation results, and has built-in multiple different analysis algorithms including minimum confidence analysis, maximum weight analysis and maximum optimization algorithm. It gives the optimization results of different algorithms and gives suggestions for model improvement.

2. A Modelica model confidence assessment system according to claim 1, characterized in that: The evaluation system supports the functions of adding sub-items, deleting, selecting evaluation algorithms, and assigning weights in the right-click menu of the root node element.

3. A Modelica model confidence assessment system according to claim 1, characterized in that: The evaluation system supports the configuration of basic attributes, expected values, and attention items in the node attribute column.

4. A Modelica model confidence assessment system according to claim 1, characterized in that: The evaluation system supports the functions of adding sub-items, deleting, displaying curves, selecting evaluation algorithms, and assigning weights in the right-click menu of intermediate node elements.

5. A Modelica model confidence assessment system according to claim 1, characterized in that: The evaluation system supports the functions of adding sub-items, deleting, and displaying curves in the right-click menu of the terminal node.

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