CN113239498A - Parameter calibration method and device of engine model and storage medium - Google Patents
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Abstract
The application discloses a parameter calibration method and device of an engine model and a storage medium, relates to the technical field of engines, and can realize rapid calibration of various parameters related to the engine model. The method comprises the following steps: acquiring at least three groups of parameters to be calibrated; each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used for representing the closeness degree of the performance parameter measured value of the engine model and the performance parameter target value; determining fitness values of at least three groups of parameters to be calibrated based on the drift density estimation index; the fitness value is used for representing the excellent degree of the performance parameter measured value corresponding to the parameter to be calibrated; and iterating at least three groups of parameters to be calibrated according to the genetic algorithm and the fitness value thereof, and determining the calibrated parameters based on the iteration result.
Description
Technical Field
The embodiment of the application relates to the technical field of engines, in particular to a parameter calibration method and device of an engine model and a storage medium.
Background
When the engine is designed in a factory, a large number of reliability tests need to be carried out through an engine model, and whether the engine model can normally run under different test conditions needs to be tested. For example, taking an aircraft engine as an example, the engine is required to stably operate at different flight speeds, different temperatures and different pressures, that is, values of parameters such as a fan content outlet pressure test value, a fan content outlet pressure test value and a thrust test value of the aircraft engine are required to meet expectations under different test conditions, and the parameters need to be calibrated under the condition that the expectations are not met.
It can be seen that the engine model involves many parameters, and calibration of various parameters is involved in parameter calibration, and high-dimensional calibration results in a reduced calibration rate, and is difficult to meet the timeliness requirement in the industry. At present, calibration methods only calibrate parameters of the engine model under a certain test condition, however, the calibration methods often make the parameters of the engine model under other test conditions out of expectation. Thus. The parameter calibration method of the engine model is urgently needed to be provided, and the calibration parameters of the engine model under various test conditions can meet expectations under the condition that the industrial timeliness requirement is met.
Disclosure of Invention
The application provides a parameter calibration method, a parameter calibration device and a storage medium of an engine model, which can be used for rapidly calibrating various parameters related to the engine model.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method of calibrating parameters of an engine model, comprising: acquiring at least three groups of parameters to be calibrated; each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used for representing the closeness degree of the performance parameter measured value of the engine model and the performance parameter target value; determining fitness values of at least three groups of parameters to be calibrated based on the drift density estimation index; the fitness value is used for representing the excellent degree of the performance parameter measured value corresponding to the parameter to be calibrated; and based on the fitness value, iterating at least three groups of parameters to be calibrated by adopting a genetic algorithm, and determining the calibrated parameters based on an iteration result.
In the technical scheme provided by the application, the performance calibration parameters in the parameters to be calibrated can represent the closeness degree of the performance parameter measurement value and the performance parameter target value of the engine model, so that the parameter calibration of the engine model can be realized based on the parameters to be calibrated. In addition, the fitness value of each group of parameters to be calibrated is determined through the drift density estimation index, and the fitness value represents the excellent degree of the performance parameter measurement value corresponding to the group of parameters to be calibrated, so that the calibrated parameters determined by iteration of the parameters to be calibrated by using the fitness value as a reference through a genetic algorithm are calibration results obtained by analyzing all the performance calibration parameters under different test conditions in the parameters to be calibrated. Therefore, the technical scheme of the application can calibrate various parameters related to the engine model under different test conditions while consuming relatively small computing resources.
Optionally, in a possible design, the "determining the fitness values of the at least three sets of parameters to be calibrated based on the drift density estimation index" may include:
determining offset indexes between the first parameter to be calibrated and the rest groups of parameters to be calibrated based on the drift density estimation index; the offset index is used for representing the size relation and the size deviation between the performance calibration parameter of the first parameter to be calibrated and the performance calibration parameters of the other groups of parameters to be calibrated; the first parameter to be calibrated is any one group of parameters to be calibrated in at least three groups of parameters to be calibrated;
and determining the fitness value of the first parameter to be calibrated according to the offset index.
Optionally, in another possible design manner, the "iterating at least three sets of parameters to be calibrated by using a genetic algorithm based on the fitness value, and determining the calibrated parameters based on the iteration result" may include: iterating at least three groups of parameters to be calibrated according to the fitness value and the genetic algorithm to obtain N groups of parameters to be calibrated which meet iteration cutoff conditions; n is a positive integer; and determining calibrated parameters from the N groups of parameters to be calibrated according to the application scene of the engine model and the performance calibration parameters in the N groups of parameters to be calibrated.
Optionally, in another possible design manner, the "iterating at least three sets of parameters to be calibrated by using a genetic algorithm based on the fitness value to obtain N sets of parameters to be calibrated that satisfy an iteration cutoff condition" may include:
step A: iterating at least three groups of parameters to be calibrated by adopting a parent selection algorithm based on the fitness value to obtain a first generation iteration result;
and B: processing parameters to be calibrated in the first generation iteration result according to a genetic crossover algorithm to obtain a second generation iteration result;
and C: processing the parameters to be calibrated in the second generation iteration result according to a genetic variation algorithm to obtain a third generation iteration result;
determining the parameters to be calibrated in the third generation iteration result as N groups of parameters to be calibrated under the condition that the third generation iteration result meets the iteration cutoff condition;
and under the condition that the third generation iteration result is determined not to meet the iteration cutoff condition, determining at least three new groups of parameters to be calibrated from the third generation iteration result and the at least three groups of parameters to be calibrated according to the fitness value, and then repeatedly executing the steps A to C until the iteration cutoff condition is met.
Optionally, in another possible design, the "iteration cutoff condition" may be: and the numerical values of the performance calibration parameters in the parameters to be calibrated in the third generation iteration result are all smaller than a preset threshold value.
Optionally, in another possible design manner, the "iterating at least three sets of parameters to be calibrated by using a parent selection algorithm based on the fitness value to obtain a first iteration result" includes:
step D: selecting two groups of parameters to be calibrated from at least three groups of parameters to be calibrated;
step E: according to the fitness value, determining a group of parameters to be calibrated from two groups of parameters to be calibrated as the parameters to be calibrated in the first generation iteration result;
and under the condition that the number of the sets of the parameters to be calibrated in the first generation iteration result does not reach a preset value, or the iteration time is greater than or equal to the preset duration, or the iteration times is greater than or equal to the preset times, repeating the step D and the step E until the number of the sets of the parameters to be calibrated in the first generation iteration result reaches the preset value.
Optionally, in another possible design, the "acquiring at least three sets of parameters to be calibrated" may include:
acquiring at least three groups of measurement parameters output by an engine model; each set of measurement parameters comprises at least one performance parameter measurement value under a test condition, and each test condition comprises at least one type of performance parameter measurement value;
and determining at least three groups of parameters to be calibrated corresponding to the at least three groups of measurement parameters according to the performance parameter target values.
In a second aspect, the present application provides a parameter calibration apparatus for an engine model, comprising: the device comprises an acquisition module and a determination module;
specifically, the acquiring module is used for acquiring at least three groups of parameters to be calibrated; each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used for representing the closeness degree of the performance parameter measured value of the engine model and the performance parameter target value;
the determining module is used for determining the fitness values of at least three groups of parameters to be calibrated based on the drift density estimation index; the fitness value is used for representing the excellent degree of the performance parameter measured value corresponding to the parameter to be calibrated;
the determining module is further used for iterating at least three groups of parameters to be calibrated by adopting a genetic algorithm based on the fitness value, and determining the calibrated parameters based on an iteration result.
Optionally, in a possible design manner, the determining module is specifically configured to:
determining offset indexes between the first parameter to be calibrated and the rest groups of parameters to be calibrated based on the drift density estimation index; the offset index is used for representing the size relation and the size deviation between the performance calibration parameter of the first parameter to be calibrated and the performance calibration parameters of the other groups of parameters to be calibrated; the first parameter to be calibrated is any one group of parameters to be calibrated in at least three groups of parameters to be calibrated;
and determining the fitness value of the first parameter to be calibrated according to the offset index.
Optionally, in another possible design manner, the determining module is specifically configured to: iterating at least three groups of parameters to be calibrated by adopting a genetic algorithm based on the fitness value to obtain N groups of parameters to be calibrated which meet iteration cutoff conditions; n is a positive integer; and determining calibrated parameters from the N groups of parameters to be calibrated according to the application scene of the engine model and the performance calibration parameters in the N groups of parameters to be calibrated.
Optionally, in another possible design manner, the determining module is specifically configured to:
step A: iterating at least three groups of parameters to be calibrated by adopting a parent selection algorithm based on the fitness value to obtain a first generation iteration result;
and B: processing parameters to be calibrated in the first generation iteration result according to a genetic crossover algorithm to obtain a second generation iteration result;
and C: processing the parameters to be calibrated in the second generation iteration result according to a genetic variation algorithm to obtain a third generation iteration result;
determining the parameters to be calibrated in the third generation iteration result as N groups of parameters to be calibrated under the condition that the third generation iteration result meets the iteration cutoff condition;
and under the condition that the third generation iteration result is determined not to meet the iteration cutoff condition, determining at least three new groups of parameters to be calibrated from the third generation iteration result and the at least three groups of parameters to be calibrated according to the fitness value, and then repeatedly executing the steps A to C until the iteration cutoff condition is met.
Optionally, in another possible design, the "iteration cutoff condition" may be: and the numerical values of the performance calibration parameters in the parameters to be calibrated in the third generation iteration result are all smaller than a preset threshold value.
Optionally, in another possible design manner, the determining module is specifically configured to:
step D: selecting two groups of parameters to be calibrated from at least three groups of parameters to be calibrated;
step E: according to the fitness value, determining a group of parameters to be calibrated from two groups of parameters to be calibrated as the parameters to be calibrated in the first generation iteration result;
and under the condition that the number of the sets of the parameters to be calibrated in the first generation iteration result does not reach a preset value, or the iteration time is greater than or equal to the preset duration, or the iteration times is greater than or equal to the preset times, repeating the step D and the step E until the number of the sets of the parameters to be calibrated in the first generation iteration result reaches the preset value.
Optionally, in another possible design manner, the obtaining module is specifically configured to:
acquiring at least three groups of measurement parameters output by an engine model; each set of measurement parameters comprises at least one performance parameter measurement value under a test condition, and each test condition comprises at least one type of performance parameter measurement value;
and determining at least three groups of parameters to be calibrated corresponding to the at least three groups of measurement parameters according to the performance parameter target values.
In a third aspect, the present application provides a parameter calibration apparatus for an engine model, including a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; the processor executes the computer-executable instructions stored in the memory to cause the parameter calibration device of the engine model to perform the parameter calibration method of the engine model as provided in the first aspect described above when the parameter calibration device of the engine model is operating.
Optionally, the parameter calibration device of the engine model may further include a transceiver, and the transceiver is configured to perform the steps of transceiving data, signaling or information, for example, acquiring at least three sets of parameters to be calibrated, under the control of the processor of the parameter calibration device of the engine model.
Further alternatively, the parameter calibration device of the engine model may be a physical machine for implementing parameter calibration of the engine model, or may be a part of the device in the physical machine, for example, a system on chip in the physical machine. The parametric calibration device for supporting the engine model performs the functions referred to in the first aspect, for example, receives, transmits or processes data and/or information referred to in the above-mentioned parametric calibration method for the engine model. The chip system includes a chip and may also include other discrete devices or circuit structures.
In a fourth aspect, the present application provides a computer readable storage medium having instructions stored thereon, which when executed by a computer, cause the computer to perform the method of calibrating parameters of an engine model as provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the method of calibrating parameters of an engine model as provided in the first aspect.
It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer readable storage medium may be packaged with the processor of the parameter calibration device of the engine model, or may be packaged separately from the processor of the parameter calibration device of the engine model, which is not limited in this application.
For the descriptions of the second, third, fourth and fifth aspects in this application, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to the beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the parameter calibration means of the above-described engine model do not limit the devices or functional modules themselves, which may appear under other names in practical implementations. Insofar as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for calibrating parameters of an engine model according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of another method for calibrating parameters of an engine model according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating a method for calibrating parameters of an engine model according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating a method for calibrating parameters of an engine model according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a parameter calibration apparatus for an engine model according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a parameter calibration device of another engine model according to an embodiment of the present application.
Detailed Description
The following describes in detail a parameter calibration method, a parameter calibration device, and a storage medium of an engine model provided in embodiments of the present application with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, 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 limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
When the engine is designed in a factory, a large number of reliability tests need to be carried out through an engine model, and whether the engine model can normally run under different test conditions needs to be tested. For example, in an aircraft engine as an example, the engine is required to stably operate at different flight speeds, different temperatures and different pressures, that is, values of parameters such as a fan content outlet pressure test value, a fan content outlet pressure test value and a thrust test value of the aircraft engine are required to meet expectations under different test conditions, and in the case that the expectations are not met, the parameters need to be calibrated.
It can be seen that the engine model involves many parameters, and calibration of various parameters is involved in parameter calibration, and high-dimensional calibration results in a reduced calibration rate, and is difficult to meet the timeliness requirement in the industry. At present, calibration methods only calibrate parameters of the engine model under a certain test condition, however, the calibration methods often make the parameters of the engine model under other test conditions out of expectation. Thus. The parameter calibration method of the engine model is urgently needed to be provided, and the calibration parameters of the engine model under various test conditions can meet expectations under the condition that the industrial timeliness requirement is met.
In view of the problems in the prior art, embodiments of the present application provide a method, an apparatus, and a storage medium for calibrating parameters of an engine model, where a shift-based sensitivity estimation (SDE) is used to determine a fitness value of each set of parameters to be calibrated, and the fitness value represents a degree of goodness of a performance parameter measurement value corresponding to the set of parameters to be calibrated, so that a calibrated parameter determined by iterating the parameters to be calibrated using the fitness value as a reference using a genetic algorithm is a calibration result obtained by analyzing all performance calibration parameters under different test conditions in the parameters to be calibrated. Therefore, the technical scheme of the application can calibrate various parameters related to the engine model under different test conditions while consuming relatively small computing resources.
The parameter calibration method of the engine model provided by the embodiment of the application can be applied to a parameter calibration device of the engine model. The parameter calibration device of the engine model may be a physical machine (e.g., a server), or may be a Virtual Machine (VM) deployed on the physical machine. The parameter calibration device of the engine model is used for acquiring the measurement parameters output by the engine model and realizing the calibration of the parameters.
The following describes a parameter calibration method of the engine model provided in the present application.
Referring to fig. 1, the parameter calibration method for the engine model provided by the embodiment of the application comprises steps S101 to S103:
s101, acquiring at least three groups of parameters to be calibrated by a parameter calibration device of the engine model.
Each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used to characterize the closeness of the engine model's measured performance parameter values to the target performance parameter values.
The environmental index required varies under different test conditions. The environmental index may include temperature, air pressure, and application speed, among others. For example, in the case of an aircraft engine, the environmental index may be temperature, air pressure, and flight speed, that is, parameters of an engine model of the aircraft engine need to be calibrated, so that the aircraft engine can stably operate at different flight speeds, different temperatures, and different air pressures.
The types of performance calibration parameters may be different for different types of engine models. For example, taking an aircraft engine as an example, the performance calibration parameter may correspond to a fan content outlet pressure test value, a thrust test value, and the like.
Optionally, in a possible implementation, the parameter calibration device of the engine model may obtain at least three sets of measured parameters output by the engine model; and then determining at least three groups of parameters to be calibrated corresponding to the at least three groups of measurement parameters according to the performance parameter target values.
Wherein each set of measurement parameters comprises at least one performance parameter measurement under a test condition, and each test condition comprises at least one type of performance parameter measurement.
Optionally, the parameter to be calibrated may be an error between the measured parameter and the target value of the performance parameter, and the calibration of the parameter to be calibrated in the embodiment of the present application is also a calibration of the error of the measured parameter. For example, the embodiment of the present application provides a method for determining a parameter to be calibrated corresponding to a measured parameter according to a target value of a performance parameter. Referring to table 1, a set of measurement parameters output by an engine model of an engine is provided, the measurement parameters are performance parameter measurement values of the engine under four different test conditions, and each test condition comprises three types of performance parameter measurement values, namely a fuel flow test value, a thrust test value and a low-pressure turbine front pressure test value:
TABLE 1
| Fuel flow rate test value | Thrust test value | Low pressure turbine front pressure test value | |
| Test Condition A | 1 | 5 | 1 |
| Test Condition B | 2 | 6 | 2 |
| Test Condition C | 3 | 7 | 3 |
| Test Condition D | 4 | 8 | 4 |
The performance parameter target value is an expected value artificially determined in advance according to the test condition and the application scenario. Illustratively, referring to Table 2, a list of performance parameter target values corresponding to the engine models of Table 1 is provided:
TABLE 2
| Target fuel flow | Target thrust value | Low pressure turbine front pressure target value | |
| Test Condition A | 1.1 | 5.2 | 1.1 |
| Test Condition B | 2.1 | 6.2 | 2.1 |
| Test Condition C | 3.1 | 7.2 | 3.1 |
| Test Condition D | 4.1 | 8.2 | 4.1 |
For example, taking the fuel flow test value under the test condition a as an example, the fuel flow test value can be obtained according to the expression: and determining the parameter to be calibrated by the fuel flow target value-fuel flow test value I/fuel flow target value. Similarly, the parameters to be calibrated under other test conditions are calculated to obtain a set of parameters to be tested as shown in table 3:
TABLE 3
It is understood that the numerical values of all the parameters mentioned in the above examples are only examples, and in practical applications, the measured value and the target value of the performance parameter output by the engine model under a specific scenario are taken as the standard, and the embodiment of the present application does not limit this.
In practical application, the interval range of the performance parameter measurement value output by the engine model may be very large, and in order to improve the calibration rate, the parameter to be calibrated may be a parameter subjected to normalization processing. For example, the parameters to be calibrated for preprocessing (i.e., the parameters in table 3) may be determined according to the measured values and the target values of the performance parameters, and then the parameters in the same dimension (corresponding to the same test condition and the same type of performance parameters) of each set of the parameters to be calibrated for preprocessing are normalized to obtain the parameters to be calibrated.
For example, the parameter calibration device of the engine model determines P groups of parameters to be calibrated for preprocessing, that is, determines the parameters in the P groups of table 3, and for example, by processing the parameters to be calibrated for the fuel flow under the test condition a, the parameter to be calibrated for the fuel flow in the P groups with the minimum value can be determined
And maximum of
Then the following may be followed according to the expression:
and processing each fuel flow to-be-calibrated parameter in the group P to obtain the normalized to-be-calibrated parameter. Wherein i represents a group in the P group.
S102, the parameter calibration device of the engine model determines the fitness values of at least three groups of parameters to be calibrated based on the drift density estimation index.
Wherein the fitness value is used to characterize how well the performance parameter measurement corresponding to the parameter to be calibrated is. Illustratively, taking the set of parameters to be calibrated shown in table 1 as an example, the fitness value of the set of parameters to be calibrated is used to characterize the overall goodness of all the measured values in table 1.
Optionally, in a possible implementation manner, the parameter calibration device of the engine model may determine, based on the drift density estimation indicator, offset indexes between the first parameter to be calibrated and the remaining sets of parameters to be calibrated; the offset index is used for representing the size relation and the size deviation between the performance calibration parameter of the first parameter to be calibrated and the performance calibration parameters of the other groups of parameters to be calibrated; and then determining the fitness value of the first parameter to be calibrated according to the offset index.
The first parameter to be calibrated is any one of at least three groups of parameters to be calibrated;
for example, all sets of parameters to be calibrated obtained by the parameter calibration device of the engine model may be regarded as a population P, a and b represent two different individuals in the population P, that is, represent two different sets of parameters to be calibrated, respectively, if a and b are both m-dimensional data, that is, a and b both include m performance calibration parameters, and if d (a, b) represents an index of deviation between a and b for a, d (a, b) may be determined according to expression (1):
where j represents the order of m performance calibration parameters in a and b.
Similarly, the deviation indexes between a and all individuals except a in the population P can be determined according to expression (1), and then the smallest one of all the determined deviation indexes is determined as the fitness value of a. For example, if the fitness value of a is expressed by SDE (a, P), SDE (a, P) may be determined according to expression (2):
SDE(a,P)＝mina∈P,a≠b(d(a,b)) (2)
s103, iterating at least three groups of parameters to be calibrated by adopting a genetic algorithm based on the fitness value and determining the calibrated parameters based on an iteration result.
Optionally, in a possible implementation manner, the parameter calibration device of the engine model may iterate at least three groups of parameters to be calibrated by using a genetic algorithm based on the fitness value to obtain N groups of parameters to be calibrated that satisfy an iteration cutoff condition; and then, according to the application scene of the engine model and the performance calibration parameters in the N groups of parameters to be calibrated, determining calibrated parameters from the N groups of parameters to be calibrated.
Wherein N is a positive integer.
Because the expected requirements for the parameter values of the engine model in different application scenarios are different, for example, in some scenarios, the requirement for one performance calibration parameter of the parameters to be calibrated may be higher, so that the calibrated parameter may be determined according to the application scenarios. For example, if the requirement for the thrust test value is high in a certain application scenario, after N sets of parameters to be calibrated are obtained, a set of parameters with the minimum thrust parameter to be calibrated in the N sets of parameters to be calibrated may be determined as the calibrated parameters.
Of course, in practical application, the calibrated parameter may also be determined from the N sets of parameters to be calibrated based on other standards, which is not limited in the embodiment of the present application.
Optionally, the embodiment of the present application further provides a method for iterating at least three sets of parameters to be calibrated by using a genetic algorithm based on the fitness value. Specifically, the method comprises the following steps: step A: iterating at least three groups of parameters to be calibrated by adopting a parent selection algorithm based on the fitness value to obtain a first generation iteration result; and B: processing parameters to be calibrated in the first generation iteration result according to a genetic crossover algorithm to obtain a second generation iteration result; and C: processing the parameters to be calibrated in the second generation iteration result according to a genetic variation algorithm to obtain a third generation iteration result; determining the parameters to be calibrated in the third generation iteration result as N groups of parameters to be calibrated under the condition that the third generation iteration result meets the iteration cutoff condition; and under the condition that the third generation iteration result is determined not to meet the iteration cutoff condition, determining at least three new groups of parameters to be calibrated from the third generation iteration result and the at least three groups of parameters to be calibrated according to the fitness value, and then repeatedly executing the steps A to C until the iteration cutoff condition is met.
For example, in a case that it is determined that the third-generation iteration result does not satisfy the iteration cutoff condition, the third-generation iteration result and some of the at least three previously acquired parameters to be calibrated, which have the largest fitness value, may be determined as new at least three sets of parameters to be calibrated.
Optionally, in a possible implementation, the iteration cutoff condition is: and the numerical values of the performance calibration parameters in the parameters to be calibrated in the third generation iteration result are all smaller than a preset threshold, or the iteration time is greater than or equal to a preset duration, or the iteration times are greater than or equal to a preset number. It is to be understood that, in practical applications, the iteration cutoff condition may also be other conditions, which is not limited in this application.
The preset threshold may be a threshold determined in advance by a person, the preset duration may be a duration determined in advance by a person, and the preset times may be times determined in advance by a person, which is not limited in the embodiment of the present application.
Optionally, an embodiment of the present application further provides a method for obtaining a first-generation iteration result by iterating at least three sets of parameters to be calibrated by using a parent selection algorithm based on a fitness value, where the method includes: step D: selecting two groups of parameters to be calibrated from at least three groups of parameters to be calibrated; step E: according to the fitness value, determining a group of parameters to be calibrated from two groups of parameters to be calibrated as the parameters to be calibrated in the first generation iteration result; and D, under the condition that the number of the sets of the parameters to be calibrated in the first generation iteration result does not reach a preset value, repeating the step D and the step E until the number of the sets of the parameters to be calibrated in the first generation iteration result reaches the preset value.
Wherein the preset value is a value determined in advance by a person. For example, the preset value may be the number of sets of all the parameters to be calibrated.
In one possible implementation, two sets of parameters to be calibrated may be randomly selected from at least three sets of parameters to be calibrated.
For example, the parameter to be calibrated in the first generation iteration result may be the one with the higher fitness value in the two sets of parameters to be calibrated.
The embodiment of the present application is taken as an example, the parent selection algorithm selects a binary tournament algorithm, and it can be understood that in practical applications, other types of parent selection algorithms may also be selected, which is not limited in the embodiment of the present application.
Optionally, the embodiment of the present application further provides a method for processing a parameter to be calibrated in a first generation iteration result according to a genetic crossover algorithm to obtain a second generation iteration result. For example, each group of parameters to be calibrated in the first-generation iteration result may be used as an individual in the population obtained from the first-generation iteration result
And
representing any two individuals in the population, then can be paired
And
performing intersection to obtain inherited parent-parent characteristicsSexual two individuals
And
specifically, it can be obtained according to expression (3)
And
wherein,
denotes the ith target value, β, of the individual x in the t generationqiFor the adjustment factor, it can be determined according to expression (4):
uiis [0, 1 ]]Uniformly distributed random numbers, η betweencMay be a parameter determined in advance by an operator for controlling the degree of closeness between parent and child, and ηcThe larger the value of the parameter(s), the higher the similarity between the parent and child generations.
Similarly, all the parameters to be calibrated in the first generation iteration result may be processed in an intersecting manner to obtain a second generation iteration result.
Optionally, an embodiment of the present application further provides a method for processing a parameter to be calibrated in a second generation iteration result according to a genetic variation algorithm to obtain a third generation iteration result. For example, each set of parameters to be calibrated in the second-generation iteration result may be used as an individual in the population obtained by the second-generation iteration result. By using
Representing any individual in the population, the individual can be subjected to variation processing to obtain the individual in the third generation iteration result
Specifically, it can be obtained according to expression (5)
Wherein,
and
respectively representing the upper bound and the lower bound of the ith parameter in each group of parameters to be calibrated of the engine model,
for the adjustment factor, it can be determined according to expression (6):
wherein u isiIs [0, 1 ]]Uniformly distributed random numbers, η betweenmuMay be a parameter determined in advance by an operator for controlling the degree of closeness between parent and child, and ηmuThe larger the value of the parameter(s), the higher the similarity between the parent and child generations.
Similarly, all the parameters to be calibrated in the second generation iteration result can be processed in an intersecting manner to obtain a third generation iteration result.
In the technical scheme provided by the embodiment of the application, the performance calibration parameter in the to-be-calibrated parameter can represent the degree of closeness between the performance parameter measurement value and the performance parameter target value of the engine model, so that the parameter calibration of the engine model can be realized based on the to-be-calibrated parameter. In addition, the fitness value of each group of parameters to be calibrated is determined through the drift density estimation index, and the fitness value represents the excellent degree of the performance parameter measurement value corresponding to the group of parameters to be calibrated, so that the calibrated parameters determined by iteration of the parameters to be calibrated by using the fitness value as a reference through a genetic algorithm are calibration results obtained by analyzing all the performance calibration parameters under different test conditions in the parameters to be calibrated. Therefore, the technical scheme provided by the embodiment of the application can be used for calibrating various parameters related to the engine model under different test conditions while consuming relatively small computing resources.
In summary, as shown in fig. 2, step S102 in fig. 1 can be replaced by steps S1021-S1022:
s1021, the parameter calibration device of the engine model determines the offset indexes between the first parameter to be calibrated and the rest groups of parameters to be calibrated based on the drift density estimation index.
S1022, the parameter calibration device of the engine model determines the fitness value of the first parameter to be calibrated according to the offset index.
Alternatively, as shown in fig. 3, step S101 in fig. 1 may be replaced with S1011-S1012:
s1011, the parameter calibration device of the engine model acquires at least three groups of measurement parameters output by the engine model.
S1012, determining at least three groups of parameters to be calibrated corresponding to the at least three groups of measurement parameters by the parameter calibration device of the engine model according to the performance parameter target values.
Optionally, as shown in fig. 4, an embodiment of the present application further provides a parameter calibration method for an engine model, including S401-S4011:
s401, the parameter calibration device of the engine model acquires at least three groups of parameters to be calibrated.
S402, determining the fitness values of at least three groups of parameters to be calibrated based on the drift density estimation index by the parameter calibration device of the engine model.
S403, the parameter calibration device of the engine model randomly selects two groups of parameters to be calibrated from at least three groups of parameters to be calibrated.
S404, determining a group of parameters to be calibrated from two groups of parameters to be calibrated by a parameter calibration device of the engine model according to the fitness value, wherein the group of parameters to be calibrated is used as the parameters to be calibrated in the first generation iteration result.
S405, the parameter calibration device of the engine model judges whether the number of sets of parameters to be calibrated in the first generation iteration result reaches a preset value.
Returning to re-execute the step S403 under the condition that the group number of the parameters to be calibrated in the first generation iteration result does not reach the preset value; if the number of sets of parameters to be calibrated in the first iteration result reaches the preset value, step S406 is executed.
S406, the parameter calibration device of the engine model processes the parameters to be calibrated in the first generation iteration result according to the genetic crossover algorithm to obtain a second generation iteration result.
And S407, processing the parameter to be calibrated in the second generation iteration result by the parameter calibration device of the engine model according to the genetic variation algorithm to obtain a third generation iteration result.
And S408, judging whether the third generation iteration result meets an iteration cutoff condition by the parameter calibration device of the engine model.
Executing step S4010 when the third generation iteration result meets the iteration cutoff condition; in the case where the third generation iteration result does not satisfy the iteration cutoff condition, step S409 is executed.
And S409, determining new at least three groups of parameters to be calibrated from the third generation iteration result and the at least three groups of parameters to be calibrated by the parameter calibration device of the engine model according to the fitness value.
And after step S409, returns to re-execution S402.
S4010, the parameter calibration device of the engine model determines the parameters to be calibrated in the third generation iteration result as N groups of parameters to be calibrated.
S4011, determining calibrated parameters from the N groups of parameters to be calibrated according to the application scene of the engine model and performance calibration parameters in the N groups of parameters to be calibrated by the parameter calibration device of the engine model.
As shown in fig. 5, an embodiment of the present application further provides a parameter calibration device for an engine model, including: an acquisition module 11 and a determination module 12.
The obtaining module 11 executes S101 in the above method embodiment, and the determining module 12 executes S102 and S103 in the above method embodiment.
Specifically, the obtaining module 11 is configured to obtain at least three sets of parameters to be calibrated; each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used for representing the closeness degree of the performance parameter measured value of the engine model and the performance parameter target value;
a determining module 12, configured to determine fitness values of at least three sets of parameters to be calibrated based on the drift density estimation indicator; the fitness value is used for representing the excellent degree of the performance parameter measured value corresponding to the parameter to be calibrated;
the determining module 12 is further configured to iterate at least three sets of parameters to be calibrated by using a genetic algorithm based on the fitness value, and determine calibrated parameters based on an iteration result.
Optionally, in a possible implementation manner, the determining module 12 is specifically configured to:
determining offset indexes between the first parameter to be calibrated and the rest groups of parameters to be calibrated based on the drift density estimation index; the offset index is used for representing the size relation and the size deviation between the performance calibration parameter of the first parameter to be calibrated and the performance calibration parameters of the other groups of parameters to be calibrated; the first parameter to be calibrated is any one group of parameters to be calibrated in at least three groups of parameters to be calibrated;
and determining the fitness value of the first parameter to be calibrated according to the offset index.
Optionally, in another possible implementation manner, the determining module 12 is specifically configured to:
iterating at least three groups of parameters to be calibrated by adopting a genetic algorithm based on the fitness value to obtain N groups of parameters to be calibrated which meet iteration cutoff conditions; n is a positive integer;
and determining calibrated parameters from the N groups of parameters to be calibrated according to the application scene of the engine model and the performance calibration parameters in the N groups of parameters to be calibrated.
Optionally, in another possible implementation manner, the determining module 12 is specifically configured to:
step A: iterating at least three groups of parameters to be calibrated by adopting a parent selection algorithm based on the fitness value to obtain a first generation iteration result;
and B: processing parameters to be calibrated in the first generation iteration result according to a genetic crossover algorithm to obtain a second generation iteration result;
and C: processing the parameters to be calibrated in the second generation iteration result according to a genetic variation algorithm to obtain a third generation iteration result;
determining the parameters to be calibrated in the third generation iteration result as N groups of parameters to be calibrated under the condition that the third generation iteration result meets the iteration cutoff condition;
and under the condition that the third generation iteration result is determined not to meet the iteration cutoff condition, determining at least three new groups of parameters to be calibrated from the third generation iteration result and the at least three groups of parameters to be calibrated according to the fitness value, and then repeatedly executing the steps A to C until the iteration cutoff condition is met.
Optionally, in another possible implementation manner, the "iteration cutoff condition" may be: and the numerical values of the performance calibration parameters in the parameters to be calibrated in the third generation iteration result are all smaller than a preset threshold value.
Optionally, in another possible implementation manner, the determining module 12 is specifically configured to:
step D: selecting two groups of parameters to be calibrated from at least three groups of parameters to be calibrated;
step E: according to the fitness value, determining a group of parameters to be calibrated from two groups of parameters to be calibrated as the parameters to be calibrated in the first generation iteration result;
and under the condition that the number of the sets of the parameters to be calibrated in the first generation iteration result does not reach a preset value, or the iteration time is greater than or equal to the preset duration, or the iteration times is greater than or equal to the preset times, repeating the step D and the step E until the number of the sets of the parameters to be calibrated in the first generation iteration result reaches the preset value.
Optionally, in another possible implementation manner, the obtaining module 11 is specifically configured to:
acquiring at least three groups of measurement parameters output by an engine model; each set of measurement parameters comprises at least one performance parameter measurement value under a test condition, and each test condition comprises at least one type of performance parameter measurement value;
and determining at least three groups of parameters to be calibrated corresponding to the at least three groups of measurement parameters according to the performance parameter target values.
Optionally, the parameter calibration device of the engine model may further include a storage module for storing program codes and the like of the parameter calibration device of the engine model.
As shown in fig. 6, the embodiment of the present application further provides a parameter calibration device for an engine model, which includes a memory 41, a processor 42, a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the parameter calibration device of the engine model is operating, the processor 42 executes the computer-executable instructions stored in the memory 41 to cause the parameter calibration device of the engine model to perform the parameter calibration method of the engine model as provided in the above-described embodiments.
In particular implementations, processor 42(42-1 and 42-2) may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 6, as one example. And as an example, the parameter calibration device of the engine model may include a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 6. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. Processor 42 may calibrate various functions of the device by running or executing software programs stored in memory 41, and invoking data stored in memory 41, parameters of the engine model.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
As an example, in connection with fig. 5, the function performed by the acquisition module in the parameter calibration device of the engine model is the same as the function performed by the receiving unit in fig. 6, the function performed by the determination module in the parameter calibration device of the engine model is the same as the function performed by the processor in fig. 6, and the function performed by the storage module in the parameter calibration device of the engine model is the same as the function performed by the memory in fig. 6.
For the explanation of the related contents in this embodiment, reference may be made to the above method embodiments, which are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the application also provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer is enabled to execute the parameter calibration method of the engine model provided by the embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of calibrating parameters of an engine model, comprising:
acquiring at least three groups of parameters to be calibrated; each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used for representing the closeness degree of the performance parameter measured value of the engine model and the performance parameter target value;
determining fitness values of the at least three groups of parameters to be calibrated based on the drift density estimation index; the fitness value is used for representing the excellent degree of the performance parameter measured value corresponding to the parameter to be calibrated;
and iterating the at least three groups of parameters to be calibrated by adopting a genetic algorithm based on the fitness value, and determining the calibrated parameters based on an iteration result.
2. The method of claim 1, wherein determining fitness values for the at least three sets of parameters to be calibrated based on a drift density estimation indicator comprises:
determining offset indexes between the first parameter to be calibrated and the rest groups of parameters to be calibrated based on the drift density estimation index; the offset index is used for representing the size relation and the size deviation between the performance calibration parameter of the first parameter to be calibrated and the performance calibration parameters of the rest groups of parameters to be calibrated; the first parameter to be calibrated is any one group of parameters to be calibrated in the at least three groups of parameters to be calibrated;
and determining the fitness value of the first parameter to be calibrated according to the offset index.
3. The method of claim 1, wherein the iterating the at least three sets of parameters to be calibrated using a genetic algorithm based on the fitness value, and determining the calibrated parameters based on the iteration result comprises:
iterating the at least three groups of parameters to be calibrated by adopting a genetic algorithm based on the fitness value to obtain N groups of parameters to be calibrated which meet iteration cutoff conditions; n is a positive integer;
and determining the calibrated parameters from the N groups of parameters to be calibrated according to the application scene of the engine model and the performance calibration parameters in the N groups of parameters to be calibrated.
4. The method according to claim 3, wherein the iterating the at least three sets of parameters to be calibrated by using a genetic algorithm based on the fitness value to obtain N sets of parameters to be calibrated that satisfy an iteration cutoff condition comprises:
step A: iterating the at least three groups of parameters to be calibrated by adopting a parent selection algorithm based on the fitness value to obtain a first generation iteration result;
and B: processing the parameters to be calibrated in the first generation iteration result according to a genetic crossover algorithm to obtain a second generation iteration result;
and C: processing the parameters to be calibrated in the second generation iteration result according to a genetic variation algorithm to obtain a third generation iteration result;
determining the parameters to be calibrated in the third generation iteration result as the N groups of parameters to be calibrated under the condition that the third generation iteration result is determined to meet the iteration cutoff condition;
and under the condition that the third generation iteration result is determined not to meet the iteration cutoff condition, determining at least three new groups of parameters to be calibrated from the third generation iteration result and the at least three groups of parameters to be calibrated according to the fitness value, and then repeatedly executing the steps A to C until the iteration cutoff condition is met.
5. The method of claim 4, wherein the iteration cutoff condition is: and the numerical values of the performance calibration parameters in the parameters to be calibrated in the third-generation iteration result are all smaller than a preset threshold, or the iteration time is greater than or equal to a preset duration, or the iteration times are greater than or equal to preset times.
6. The method of claim 4, wherein iterating the at least three sets of parameters to be calibrated based on the fitness value using a parent selection algorithm to obtain a first iteration result, comprises:
step D: selecting two groups of parameters to be calibrated from the at least three groups of parameters to be calibrated;
step E: determining a group of parameters to be calibrated from the two groups of parameters to be calibrated according to the fitness value, wherein the parameters to be calibrated are used as the parameters to be calibrated in the first generation iteration result;
and under the condition that the number of the groups of the parameters to be calibrated in the first generation iteration result does not reach a preset value, repeating the step D and the step E until the number of the groups of the parameters to be calibrated in the first generation iteration result reaches the preset value.
7. The method of claim 1, wherein obtaining at least three sets of parameters to be calibrated comprises:
acquiring at least three groups of measurement parameters output by the engine model; each set of measurement parameters comprises at least one type of said performance parameter measurements under each test condition;
and determining the at least three groups of parameters to be calibrated corresponding to the at least three groups of measurement parameters according to the performance parameter target values.
8. A parameter calibration device for an engine model, comprising:
the acquisition module is used for acquiring at least three groups of parameters to be calibrated; each group of parameters to be calibrated comprises at least one performance calibration parameter under a test condition, and each test condition comprises at least one type of performance calibration parameter; the performance calibration parameters are used for representing the closeness degree of the performance parameter measured value of the engine model and the performance parameter target value;
the determining module is used for determining the fitness values of the at least three groups of parameters to be calibrated based on the drift density estimation index; the fitness value is used for representing the excellent degree of the performance parameter measured value corresponding to the parameter to be calibrated;
the determining module is further configured to iterate the at least three sets of parameters to be calibrated by using a genetic algorithm based on the fitness value, and determine calibrated parameters based on an iteration result.
9. The parameter calibration device of the engine model is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
a processor executes the computer-executable instructions stored by the memory to cause the parameter calibration device of the engine model to perform the method of calibrating parameters of an engine model according to any one of claims 1 to 7 when the parameter calibration device of the engine model is running.
10. A computer-readable storage medium having stored therein instructions which, when executed by a computer, cause the computer to perform a method of calibrating parameters of an engine model according to any one of claims 1 to 7.
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