CN113449897A - Method for optimizing point sweeping of test parameters of engine bench - Google Patents
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Abstract
According to the method for scanning the optimized parameters of the engine bench test, a plurality of parameters influencing an optimized target are obtained through analyzing the original scanning data; sequencing the parameters according to the importance degree of influencing the optimization target, and simultaneously obtaining the influence value of the parameters on the optimization target; determining ranges of a plurality of fine scanning parameters according to the influence values; and determining a fine scanning area; selecting a plurality of fine scanning points in the fine scanning area; determining an optimal value of the influence of the fine scanning parameters on an optimization target; the steps of the optimized point scanning method are all realized through self-programming, the automatic optimization process of the optimization target is realized, and the optimal value of the influence on the optimization target can be intelligently obtained; in the process of the engine bench test, the influence of different parameters on the optimization target can be locked to determine the fine scanning parameters and reject the parameters which have little influence on the optimization target, so that the test workload of the bench test can be reduced, and the efficiency of the bench test can be improved.
Description
Technical Field
The invention relates to the technical field of test testing, in particular to a method for scanning points in an engine bench test parameter optimization mode.
Background
During the calibration optimization process of the engine bench test, a multi-calibration parameter scanning operation is performed on an optimization target (such as gaseous emissions, etc.), and usually, an engineer will set a multi-parameter rough step scanning matrix according to personal experience, wherein the matrix size is the product of the number of calibration parameters. And then, according to the result of the rough scanning, selecting an area with better target parameters, and finely scanning (fine scanning) the calibration parameters of the area, wherein the scanning range is the product of the number of the calibration parameters of the area. The engine bench test calibration optimization method cannot lock different calibration parameter influence sizes (only the importance of a small number of parameters can be locked even if experienced engineers face a multi-parameter complex problem), so that the scanning density of the fine scanning parameters cannot be distributed according to the importance of the parameters, all the parameters can be scanned according to the similar scanning density, even the calibration parameters which have little influence on the optimization target can be increased by mistake for fine scanning, and the test workload of the fine scanning of the parameters is greatly increased.
Disclosure of Invention
The invention aims to provide a method for optimizing and sweeping engine bench test parameters, which aims to solve the problems that in the prior art, a large amount of time and bench resources are consumed for determining an optimal combination scheme by means of manual experience, and the optimal parameter combination scheme is not easy to find out.
The invention provides an engine bench test parameter optimization point sweeping method, which comprises the following steps:
acquiring original scanning data;
analyzing and processing the original scanning data to obtain a plurality of parameters influencing an optimization target;
analyzing a plurality of parameters, sequencing the parameters according to the degree of influence on an optimization target, and obtaining the influence value of the parameters on the optimization target;
determining a plurality of fine scanning parameters according to the ranking of the importance degrees of the parameters, and determining the range of the plurality of fine scanning parameters according to the influence value;
determining a fine scanning area according to the plurality of fine scanning parameters and the range corresponding to the plurality of fine scanning parameters, and selecting a plurality of fine scanning points in the fine scanning area;
and determining the optimal value of the influence of the fine scanning parameters on the optimization target.
Optionally, in the method for scanning optimized parameters of the engine bench test, a random forest algorithm is used to analyze and process the original scanning data, so as to obtain parameters affecting an optimization target.
Optionally, in the method for optimizing the sweep point of the engine bench test parameters, the method for sorting the plurality of parameters according to the importance degree according to the degree influencing the optimization goal includes the following steps:
establishing a random forest algorithm model;
taking a plurality of the parameters and the optimization target as input variables in a random forest algorithm;
and analyzing the input variables by adopting a random forest algorithm to obtain the importance distribution of different parameters, and sequencing the parameters.
Optionally, in the method for optimizing the sweep point of the engine bench test parameters, the method for obtaining the influence value of the plurality of parameters on the optimization target comprises the following steps:
establishing a random forest algorithm model;
taking a plurality of the parameters and the optimization target as input variables in a random forest algorithm;
and analyzing the input variables by adopting a random forest algorithm to obtain the influence values of a plurality of parameters on the optimization target.
Optionally, in the method for optimizing a sweep point by using the engine bench test parameters, the method for determining the fine sweep parameter includes the following steps:
and sequentially sorting according to the importance of the parameters and the importance degree, and taking a plurality of parameters which are sorted in the front as the fine scanning parameters.
Optionally, in the method for optimizing a sweep point by using the engine bench test parameters, the method for determining the fine sweep area includes the following steps:
acquiring a plurality of fine scanning parameters and influence values corresponding to the fine scanning parameters;
combining the influence values corresponding to the fine scanning parameters to obtain a plurality of optimal combinations influencing the optimization target;
and sequencing the multiple better combinations, and selecting the optimal combination as a fine scanning area.
Optionally, in the method for optimizing the sweep points of the test parameters of the engine bench, the method for selecting a plurality of fine sweep points includes the following steps:
according to the importance ranking of the fine scanning parameters, determining different scanning point densities of different fine scanning parameters;
the scanning point density is in direct proportion to the importance of the fine scanning parameter;
and the scanning point falling into the fine scanning area is the fine scanning point.
Optionally, in the method for optimizing the sweep point of the engine bench test parameters, the method for determining the optimal value of the influence of the fine sweep parameter on the optimization target includes the following steps:
determining a scanning point range of each fine scanning parameter by combining the fine scanning area;
and determining the optimal value of the influence of the fine scanning parameters on the optimization target by combining the scanning range and the scanning density.
Optionally, in the method for optimizing the sweep point by using the engine bench test parameters, an orthogonal test method is used to determine an optimal value of the influence of the fine sweep parameter on the optimization target.
Optionally, in the method for optimizing the sweep point of the engine bench test parameters, a factorization method is adopted to determine an optimal value of the influence of the fine sweep parameter on the optimization target.
In summary, according to the method for optimizing the scanning points of the engine bench test parameters provided by the invention, a plurality of parameters influencing the optimization target are obtained through analyzing the original scanning data; sequencing the parameters according to the importance degree of influencing the optimization target, and simultaneously obtaining the influence value of the parameters on the optimization target; determining ranges of a plurality of fine scanning parameters according to the influence values; and determining a fine scanning area; selecting a plurality of fine scanning points in the fine scanning area; determining an optimal value of the influence of the fine scanning parameters on an optimization target; the steps of the optimized point scanning method are all realized through self-programming, the automatic optimization process of the optimization target is realized, and the optimal value of the influence on the optimization target can be intelligently obtained; in the process of the engine bench test, the influence of different parameters on the optimization target can be locked to determine the fine scanning parameters and reject the parameters which have little influence on the optimization target, so that the test workload of the bench test can be reduced, and the efficiency of the bench test can be improved.
Drawings
FIG. 1 is a flow chart of a method for optimizing a sweep point for engine rig test parameters provided in an embodiment of the present invention;
FIG. 2 is a flowchart of a method for ranking a plurality of the parameters according to importance according to a degree affecting an optimization objective, according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for deriving an influence value of a plurality of parameters on an optimization target according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for determining a fine scan region according to an embodiment of the present invention;
FIG. 5 is a flowchart of a method for selecting a plurality of fine scanning points according to an embodiment of the present invention;
fig. 6 is a flowchart of a method for determining an optimal value of the impact of the fine-scan parameter on the optimization objective according to an embodiment of the present invention.
Detailed Description
To further clarify the objects, advantages and features of the present invention, a method for optimizing a sweep point for engine rig test parameters in accordance with the present invention is described in further detail below with reference to FIGS. 1-6. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Referring to fig. 1, the application provides an engine bench test parameter optimization point sweeping method, comprising the following steps:
step S110: acquiring original scanning data;
step S120: analyzing and processing the original scanning data to obtain a plurality of parameters influencing an optimization target;
step S130: analyzing a plurality of parameters, sequencing the parameters according to the degree of influence on an optimization target, and obtaining the influence value of the parameters on the optimization target;
step S140: determining a plurality of fine scanning parameters according to the ranking of the importance degrees of the parameters, and determining the range of the plurality of fine scanning parameters according to the influence value;
step S150: determining a fine scanning area according to the plurality of fine scanning parameters and the range corresponding to the plurality of fine scanning parameters, and selecting a plurality of fine scanning points in the fine scanning area;
step S160: and determining the optimal value of the influence of the fine scanning parameters on the optimization target.
Obtaining a plurality of parameters influencing an optimization target through analysis of the original scanning data; sequencing the parameters according to the importance degree of influencing the optimization target, and simultaneously obtaining the influence value of the parameters on the optimization target; determining ranges of a plurality of fine scanning parameters according to the influence values; and determining a fine scanning area; selecting a plurality of fine scanning points in the fine scanning area; determining an optimal value of the influence of the fine scanning parameters on an optimization target; the steps of the optimized point scanning method are all realized through self-programming, the automatic optimization process of the optimization target is realized, and the optimal value of the influence on the optimization target can be intelligently obtained; in the process of the engine bench test, the influence of different parameters on the optimization target can be locked to determine the fine scanning parameters and reject the parameters which have little influence on the optimization target, so that the test workload of the bench test can be reduced, and the efficiency of the bench test can be improved.
Preferably, the raw scan data is analyzed and processed by a machine learning algorithm to obtain a plurality of parameters affecting the optimization goal, which will be described below by using a random forest algorithm as an example, the random forest algorithm is a multifunctional machine learning algorithm, and belongs to a combination algorithm, that is, a plurality of base algorithms are used in combination, each base algorithm is predicted separately, and the final conclusion is voted (for classification problem) or averaged (including weighted average, for regression problem) by all the base algorithms. The principle schematic diagram of the random forest algorithm is shown in the following figure, the base algorithm generally adopts decision trees, a forest is formed by a plurality of decision trees, algorithm classification results are obtained by voting of the decision trees, random processes are added to the decision trees in the generating process in the row direction and the column direction respectively, the decision trees are constructed in the row direction by using return sampling to obtain training data, and the random sampling without return is adopted in the column direction to obtain a feature subset, so that the optimal segmentation point is obtained. The random forest algorithm is a combined model, the interior of the random forest algorithm is still based on decision trees, and the random forest algorithm is different from single decision tree classification in that the random forest algorithm classifies through voting results of a plurality of decision trees, so that the random forest algorithm is not easy to generate overfitting problems, and the random forest algorithm is good at processing multi-parameter problems. The core of the random forest algorithm is randomness, which is expressed in the following 3 aspects:
1) the amount of the returned extracted data can be the same as or slightly smaller than that of the original data;
2) randomly selecting N parameters, and selecting the best attribute for splitting;
3) randomly selecting one parameter from the N best splitting parameters to split;
due to the randomness of the above 3 aspects and the characteristics of the decision tree, the random forest algorithm has the following advantages:
overfitting is not easily caused;
the noise immunity and robustness are good;
the data processing mode is simple and convenient;
input of a plurality of parameters;
the parameters can be simultaneously calculated for the optimized target influence value and analyzed for the importance of the parameters;
simultaneously outputting the influence value and the importance of the parameters;
the algorithm has the advantages, and is suitable for the application condition of multi-parameter influence of the optimization sweep point of the bench test parameters. Other similar intelligent algorithms can also be used as a bench test parameter optimization sweep data analysis method.
As shown in fig. 2, the method for ranking a plurality of parameters according to the degree of influence on the optimization goal includes the following steps:
step S131: establishing a random forest algorithm model;
step S132: taking a plurality of the parameters and the optimization target as input variables in a random forest algorithm;
step S133: and analyzing the input variables by adopting a random forest algorithm to obtain the importance distribution of different parameters, and sequencing the parameters.
As shown in fig. 3, the method for obtaining the influence value of a plurality of parameters on the optimization target comprises the following steps:
step S134: establishing a random forest algorithm model;
step S135: taking a plurality of the parameters and the optimization target as input variables in a random forest algorithm;
step S136: and analyzing the input variables by adopting a random forest algorithm to obtain the influence values of a plurality of parameters on the optimization target.
The method for determining the fine scanning parameters comprises the following steps:
and sequentially sorting according to the importance of the parameters and the importance degree, and taking a plurality of parameters which are sorted in the front as the fine scanning parameters. Specifically, the coarsely scanned bench test data is fitted through an intelligent machine algorithm, wherein the intelligent machine algorithm adopts a random forest algorithm, and the random forest algorithm can output a plurality of parameters to optimize target influence values simultaneously in the process of classifying the original data. The specific process is that a plurality of influence values of each parameter influencing the optimization target in the random forest algorithm are combined into the parameter average influence value, namely the parameter average influence value is the influence value of the parameter on the optimization target. In general, the influence values of the parameters on the optimization target are usually kept with high precision due to self-fitting through rough scan data. Therefore, whether the influence value of each parameter on the optimization target is large or not can be judged by analyzing the influence value of each parameter on the optimization target, if the influence value of a certain parameter on the optimization target is large, the influence of the parameter on the optimization target under the working condition is large, the importance of the parameter with the large influence on the optimization target is large, namely, the parameter with the large influence value on the optimization target is important, and the analysis of the working condition and the next fine sweeping selection point can be assisted by analyzing the influence values of different parameters under different working conditions.
As shown in fig. 4, the method for determining the fine scanning area includes the following steps:
s151, acquiring a plurality of fine scanning parameters and influence values corresponding to the fine scanning parameters;
s152, combining the influence values corresponding to the fine scanning parameters to obtain a better combination of a plurality of influence optimization targets;
and S153, sequencing the preferred combinations, and selecting the optimal combination as a fine scanning area.
As shown in fig. 5, the method for selecting a plurality of fine scanning points includes the following steps:
s154, determining different scanning point densities of different fine scanning parameters according to the importance sequence of the fine scanning parameters;
step S155, the scanning point density is in direct proportion to the importance of the fine scanning parameter;
and S156, the scanning point falling into the fine scanning area is the fine scanning point.
As shown in fig. 6, the method for determining the optimal value of the impact of the fine scanning parameter on the optimization goal comprises the following steps:
step S161: determining a scanning point range of each fine scanning parameter by combining the fine scanning area;
step S162: and determining the optimal value of the influence of the fine scanning parameters on the optimization target by combining the scanning range and the scanning density.
And determining the scanning point density of each fine scanning parameter by the determined parameter importance sequence, namely, one fine scanning parameter corresponds to one scanning point density, the more important fine scanning parameter adopts the more dense scanning point density, otherwise, the more sparse scanning point density is adopted, and the non-fine scanning parameter can even not be scanned.
And determining the optimal value of the influence of the fine scanning parameters on the optimization target by combining the scanning point range and the scanning density through a DOE (design of experiments) method, and greatly optimizing the test quantity in the fine scanning process. The DOE test design method is an orthogonal test method or a factorial method, namely, the optimal value of the influence of the fine scanning parameters on the optimization target is determined by the orthogonal test method; or, determining the optimal value of the influence of the fine scanning parameters on the optimization target by adopting a factorization method.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.
Claims (10)
1. A method for scanning points by optimizing test parameters of an engine bench is characterized by comprising the following steps:
acquiring original scanning data;
analyzing and processing the original scanning data to obtain a plurality of parameters influencing an optimization target;
analyzing a plurality of parameters, sequencing the parameters according to the degree of influence on an optimization target, and obtaining the influence value of the parameters on the optimization target;
determining a plurality of fine scanning parameters according to the ranking of the importance degrees of the parameters, and determining the range of the plurality of fine scanning parameters according to the influence value;
determining a fine scanning area according to the plurality of fine scanning parameters and the range corresponding to the plurality of fine scanning parameters, and selecting a plurality of fine scanning points in the fine scanning area;
and determining the optimal value of the influence of the fine scanning parameters on the optimization target.
2. The engine mount test parameter optimized sweep method of claim 1 wherein a random forest algorithm is employed to analyze and process the raw sweep data to derive parameters that affect an optimization objective.
3. The engine mount test parameter optimizing sweep method according to claim 2, wherein the method of ranking a plurality of said parameters by degree of importance in terms of degree of influence on optimization objective comprises the steps of:
establishing a random forest algorithm model;
taking a plurality of the parameters and the optimization target as input variables in a random forest algorithm;
and analyzing the input variables by adopting a random forest algorithm to obtain the importance distribution of different parameters, and sequencing the parameters.
4. The engine mount test parameter optimized sweep method of claim 2 wherein the method of deriving a plurality of said parameter impact values on an optimization target comprises the steps of:
establishing a random forest algorithm model;
taking a plurality of the parameters and the optimization target as input variables in a random forest algorithm;
and analyzing the input variables by adopting a random forest algorithm to obtain the influence values of a plurality of parameters on the optimization target.
5. The engine mount test parameter optimizing sweep method of claim 1 wherein the method of determining the fine sweep parameter comprises the steps of:
and sequentially sorting according to the importance of the parameters and the importance degree, and taking a plurality of parameters which are sorted in the front as the fine scanning parameters.
6. The engine mount test parameter optimizing sweep method of claim 1 wherein the method of determining a fine sweep area comprises the steps of:
acquiring a plurality of fine scanning parameters and influence values corresponding to the fine scanning parameters;
combining the influence values corresponding to the fine scanning parameters to obtain a plurality of optimal combinations influencing the optimization target;
and sequencing the multiple better combinations, and selecting the optimal combination as a fine scanning area.
7. The engine mount test parameter optimizing sweep method of claim 1 wherein the method of selecting a plurality of fine sweep points comprises the steps of:
according to the importance ranking of the fine scanning parameters, determining different scanning point densities of different fine scanning parameters;
the scanning point density is in direct proportion to the importance of the fine scanning parameter;
and the scanning point falling into the fine scanning area is the fine scanning point.
8. The engine rig test parameter optimization sweep method of claim 7 wherein the method of determining the optimal value of the fine sweep parameter effect on the optimization objective comprises the steps of:
determining a scanning point range of each fine scanning parameter by combining the fine scanning area;
and determining the optimal value of the influence of the fine scanning parameters on the optimization target by combining the scanning range and the scanning density.
9. The engine rig test parameter optimization sweep method of claim 8, wherein an orthogonal test method is used to determine the optimal value of the fine sweep parameter effect on the optimization objective.
10. The method of engine rig test parameter optimized sweep point of claim 8 wherein a factoring method is employed to determine the optimal value of the fine sweep parameter effect on the optimization objective.
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