CN113408070A - Method, device and equipment for determining engine parameters and storage medium - Google Patents
Method, device and equipment for determining engine parameters and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for determining engine parameters, and belongs to the technical field of aviation control. The method comprises the following steps: obtaining the parameter prediction data of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm; predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model; and under the condition that the prediction result is that the parameter prediction data of this time is effective, determining whether the parameter prediction data of this time is parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the parameter prediction data of this time based on the simulation model. By the aid of the technical scheme, the times of evaluating illegal parameters are reduced, and the efficiency of engine model correction is greatly improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of aviation, in particular to a method, a device, equipment and a storage medium for determining engine parameters.
Background
Aircraft engine model calibration is an important issue in aircraft engine design. The problem is mainly that the engine meets performance indexes on different scenes on a working line by adjusting the values of a group of parameters.
The existing methods have certain limitations when dealing with the problem of engine model calibration, and improvement is urgently needed.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for determining engine parameters, so as to realize accurate correction of engine model parameters and improve correction efficiency.
In a first aspect, an embodiment of the present invention provides a method for determining an engine parameter, including:
obtaining the parameter prediction data of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm;
predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model;
and under the condition that the prediction result is that the parameter prediction data of this time is effective, determining whether the parameter prediction data of this time is parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the parameter prediction data of this time based on the simulation model.
In a second aspect, an embodiment of the present invention further provides an apparatus for determining an engine parameter, including:
the data determination module is used for obtaining the parameter prediction data of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm;
the effectiveness prediction module is used for predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model;
and the parameter target data determining module is used for determining whether the current parameter prediction data is the parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the current parameter prediction data based on the simulation model under the condition that the prediction result is that the current parameter prediction data is effective.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of determining an engine parameter as described in any embodiment of the invention.
In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for determining an engine parameter according to any of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, the current parameter prediction data of the simulation model corresponding to the engine is obtained based on the last parameter prediction data through an intelligent optimization algorithm, then the effectiveness of the current parameter prediction data is predicted based on an effectiveness prediction model, and further whether the current parameter prediction data is the parameter target data of the engine is determined according to the comparison result between the actual working scene data of the engine and the output data of the current parameter prediction data based on the simulation model under the condition that the prediction result is that the current parameter prediction data is effective. By the technical scheme, the times of evaluating illegal parameters are reduced, and the efficiency of engine model correction is greatly improved.
Drawings
FIG. 1 is a flow chart of a method for determining engine parameters according to one embodiment of the present invention;
FIG. 2 is a flow chart of a method for determining engine parameters according to a second embodiment of the present invention;
fig. 3 is a block diagram of an engine parameter determination apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
FIG. 1 is a flow chart of a method for determining engine parameters according to one embodiment of the present invention; the embodiment can be applied to the situation of aircraft engine model correction. The method may be performed by an engine parameter determination device, which may be implemented in software and/or hardware, and may be integrated in an electronic device, such as a server, that carries out the engine parameter determination function.
As shown in fig. 1, the method specifically includes the following steps:
and S110, obtaining the parameter prediction data of the current time of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm.
The intelligent optimization algorithm can be a genetic optimization algorithm, a cross mutation algorithm and the like. So-called simulation models, i.e. models simulating the operation of the engine; optionally, the parameters in the simulation model correspond to parameters configured by the engine itself in an actual scene, and may include, but are not limited to, compressor efficiency, fan-over-pressure ratio, and the like. Furthermore, the parameters in the simulation model are multidimensional, and each parameter has a corresponding value range.
In this embodiment, the parameter prediction data is values of parameters in a set of predicted simulation models. Optionally, in this embodiment, the values of the parameters of the simulation model are adjusted to the predicted parameter data, and the input data of the engine in different working scenes is input into the simulation model, so that the simulation model can output the output data in different working scenes under the corresponding parameter data, and in addition, the simulation model can also verify the validity of the predicted parameter data and output a validity verification result. Wherein, the engine working scene can include but is not limited to taxiing, taking off and landing; the input data in different working scenes are different, for example, the input data in the takeoff scene can include takeoff height, air pressure and the like; correspondingly, the output data in different working scenarios are also different, for example, the output data in the takeoff scenario may include engine power, rotation speed, and the like.
For example, when the determination logic of the parameter target data starts to execute (i.e., when the logic provided in this embodiment is executed for the first time), the last parameter prediction data is the initial parameter prediction data (or parent parameter prediction data, which may be denoted as 0 th parameter prediction data), and at this time, the last parameter prediction data may be a group of data formed by randomly selecting a value of each parameter based on a value range of each parameter; or the last parameter prediction data at this time may also be a set of parameter data randomly selected from a plurality of sets of parameter data predicted in advance. It should be noted that the last parameter prediction data and the multiple sets of parameter data predicted in advance are verified and validated through the simulation model.
Correspondingly, in this embodiment, when the last parameter prediction data is the initial parameter prediction data, the present parameter prediction data of the simulation model corresponding to the engine, that is, the 1 st parameter prediction data, may be obtained through an intelligent optimization algorithm.
Further, in the case where the last parameter prediction data is not the parameter target data of the engine, the last parameter prediction data refers to data that is retained after the last execution of the determination logic of the parameter target data. For example, if the obtained 1 st-time parameter prediction data is not the parameter target data of the engine based on the initial parameter prediction data by the intelligent optimization algorithm, and the relative error between the output data of the 1 st-time parameter prediction data based on the simulation model and the actual working scene data of the engine is larger than the relative error between the output data of the initial parameter prediction data based on the simulation model and the actual working scene data of the engine, the 1 st-time parameter prediction data is updated by the initial parameter prediction data, that is, the last-time parameter prediction data is substantially the 0 th-time parameter prediction data. For another example, if the relative error between the output data of the 1 st parameter prediction data based on the simulation model and the actual working scene data of the engine is smaller than the relative error between the output data of the initial parameter prediction data based on the simulation model and the actual working scene data of the engine, the last parameter prediction data is the 1 st parameter prediction data.
Correspondingly, under the condition that the last parameter prediction data is not the parameter target data of the engine, the current parameter prediction data of the simulation model corresponding to the engine is obtained through an intelligent optimization algorithm based on the last parameter prediction data.
And S120, predicting the effectiveness of the parameter prediction data based on the effectiveness prediction model.
In this embodiment, the present parameter prediction data is input into the effectiveness prediction model, and the effectiveness of the present parameter prediction data is predicted.
For example, if the prediction result is that the parameter prediction data of this time is valid, S130 is executed. And if the prediction result is that the parameter prediction data of the current time is invalid, eliminating the parameter prediction data of the current time, returning to continue to execute S110, and re-determining the parameter prediction data of the current time.
For example, the 1 st order parameter prediction data, that is, the present time parameter prediction data is obtained in S110, and the 1 st order parameter prediction data is input to the validity prediction model to predict the validity of the 1 st order parameter prediction data. If the 1 st parameter prediction data is invalid, returning to step S110, predicting the current parameter prediction data again, that is, predicting the current parameter prediction data of the simulation model corresponding to the engine, that is, the 1 st parameter prediction data, based on the initial parameter prediction data by using the intelligent optimization algorithm, and continuing to perform step S120.
Optionally, the effectiveness prediction model in this embodiment may be determined as follows:
firstly, performing Latin square sampling based on a parameter value range of a simulation model to obtain a training sample; wherein, the parameter value range is set by the technicians in the field according to the actual condition of the engine. Specifically, in the parameter value range of the simulation model, a Latin method is adopted for sampling to construct a training sample. It can be understood that the Latin square sampling is adopted, so that the accuracy of the model is increased, and the generalization capability of the model is improved.
Secondly, based on the simulation model, determining the validity verification result of the training sample. Specifically, the training sample is input to the simulation model, and the simulation model outputs the validity verification result of the training sample. Wherein the validity verification result comprises valid and invalid.
And thirdly, training the neural network model by adopting the training samples and the effectiveness verification results of the training samples to obtain an effectiveness prediction model. Specifically, the validity verification result of the training sample is used as the label data of the training sample, and then the validity verification results of the training sample and the training sample are input to the neural network model for training to obtain the validity prediction model.
Further, the effectiveness prediction model in this embodiment may be constructed in an offline scenario, or in an online scenario, and dynamically updated.
And S130, under the condition that the prediction result is that the parameter prediction data of the time is effective, determining whether the parameter prediction data of the time is the parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the parameter prediction data of the time based on the simulation model.
The actual working scene data of the engine refers to working data of the engine in an actual working scene, and the actual working scene data of the engine in the embodiment is actual output data of the engine.
In this embodiment, if the output data of the simulation model in different scenes is consistent with the output data of the engine in corresponding actual working scenes or the error between the two is within a set range, the parameter value of the simulation model at this time is taken as the parameter target data.
Optionally, in this embodiment, the output data of the current parameter prediction data based on the simulation model may be obtained in the following manner: and adjusting the parameter value of the simulation model into the current parameter prediction data, and inputting the input data of the engine in different working scenes into the simulation model, so that the simulation model can output the output data in different working scenes under the current parameter prediction data.
In this embodiment, when the prediction result is that the current parameter prediction data is valid, if an absolute value of a difference between output data in the actual working scene data and output data of the current parameter prediction data based on the simulation model is less than or equal to an error set value, or is within a set range, it is determined that the current parameter prediction data is parameter target data of the engine. Wherein the error set value is set by a person skilled in the art according to actual conditions.
Optionally, under the condition that the prediction result is that the parameter prediction data of this time is valid, if an absolute value of a difference between output data in the actual working scene data and output data of the parameter prediction data of this time based on the simulation model is greater than an error set value or is not within a set range, it is determined that the parameter prediction data of this time is not parameter target data of the engine. Further, taking the absolute value of the difference between the output data in the actual working scene data and the output data of the last parameter prediction data based on the simulation model as a first total error, and taking the absolute value of the difference between the output data in the actual working scene data and the output data of the current parameter prediction data based on the simulation model as a second total error; if the second total error is smaller than the first total error, the parameter prediction data of this time is used as last parameter prediction data, and the step returns to execute S110, namely the parameter prediction data of this time is re-determined through an intelligent optimization algorithm based on the updated last parameter prediction data; if the second total error is greater than or equal to the first total error, the last parameter prediction data is reserved, and the step returns to execute S110, namely the parameter prediction data of the time is re-determined through an intelligent optimization algorithm based on the last parameter prediction data.
For a more clear description, a specific example is given, and it is assumed that this time parameter prediction data is 5 th time parameter prediction data, and last time parameter prediction data is 4 th time parameter prediction data. And if the absolute value of the difference between the output data in the actual working scene data and the output data of the 5 th parameter prediction data based on the simulation model is larger than the error set value, the 5 th parameter prediction data is not the parameter target data of the engine. Further determining the absolute value of the difference between the output data of the simulation model and the output data in the actual working scene data under the 4 th parameter prediction data, namely a first total error; determining the absolute value of the difference between the output data of the simulation model and the output data in the actual working scene data under the 5 th parameter prediction data, namely a second total error; if the second total error is smaller than the first total error, the 5 th parameter prediction data is used as the last parameter prediction data, and the step returns to execute S110, namely the current parameter prediction data (the 6 th parameter prediction data) is determined again through an intelligent optimization algorithm based on the updated last parameter prediction data (the 5 th parameter prediction data); if the second total error is greater than or equal to the first total error, the last parameter prediction data (4 th parameter prediction data) is retained, and the process returns to step S110, that is, the current parameter prediction data (6 th parameter prediction data) is re-determined through the intelligent optimization algorithm based on the last parameter prediction data (4 th parameter prediction data).
According to the technical scheme of the embodiment of the invention, the current parameter prediction data of the simulation model corresponding to the engine is obtained based on the last parameter prediction data through an intelligent optimization algorithm, then the effectiveness of the current parameter prediction data is predicted based on an effectiveness prediction model, and further whether the current parameter prediction data is the parameter target data of the engine is determined according to the comparison result between the actual working scene data of the engine and the output data of the current parameter prediction data based on the simulation model under the condition that the prediction result is that the current parameter prediction data is effective. By the technical scheme, the times of evaluating illegal parameters are reduced, and the efficiency of engine model correction is greatly improved.
Example two
Fig. 2 is a flowchart of a method for determining engine parameters according to a second embodiment of the present invention; on the basis of the above embodiment, an optional implementation scheme is provided for optimizing "determining whether the current parameter prediction data is the parameter target data of the engine according to the comparison result between the actual working scene data and the output data of the current parameter prediction data based on the simulation model under the condition that the prediction result is valid for the current parameter prediction data".
As shown in fig. 2, the method may specifically include:
s210, obtaining the parameter prediction data of the current time of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm.
And S220, predicting the effectiveness of the parameter prediction data based on the effectiveness prediction model.
S230, under the condition that the prediction result is that the current parameter prediction data is valid, judging whether the prediction result is the same as the validity verification result of the current parameter prediction data determined based on the simulation model or not, and if so, executing S240; if not, go to S250.
Optionally, since the time taken for determining the validity verification result of the parameter prediction data by the simulation model is shorter than the time taken for outputting the data of the parameter prediction data this time based on the simulation model, as an optional implementation scheme of the embodiment, if the prediction result is different from the validity verification result of the parameter prediction data this time determined based on the simulation model, the simulation model may be set to stop the simulation operation on the output data of the parameter prediction data this time.
S240, determining whether the current parameter prediction data is parameter target data of the engine according to a comparison result between the actual working scene data of the engine and output data of the current parameter prediction data based on the simulation model.
In this embodiment, the actual working scene data and the output data of the current parameter prediction data based on the simulation model are respectively input to the comparison model, the comparison model outputs a comparison result between the actual working scene data and the output data of the current parameter prediction data based on the simulation model, and then according to the comparison result, whether the current parameter prediction data is the parameter target data of the engine is determined. The comparison model is obtained by training a statistical method based on output data of historical time working scene data and historical parameter prediction data by a person skilled in the art.
Optionally, if the number of numerical value changes between the actual working scene data and the output data of the current parameter prediction data based on the simulation model is within a set range, the current parameter prediction data is used as the parameter target data of the engine. The setting range is set by a person skilled in the art according to actual conditions. Specifically, if the number of numerical changes between the actual working scene data of each dimension and the output data of the current parameter prediction data based on the simulation model is within a set range, the current parameter prediction data is used as the parameter target data of the engine. And if the numerical value change quantity between the actual working scene data with a certain dimensionality and the output data of the current parameter prediction data based on the simulation model is not in the set range, re-determining the parameter prediction data, further adopting the simulation model to predict based on the new parameter prediction data, and outputting the prediction data in the corresponding actual working scene.
Optionally, a total actual index may be determined according to data of each dimension of actual working scene data, a total simulation index may be determined according to data of each dimension of output data of the current parameter prediction data based on the simulation model, and if a difference between the actual index and the simulation index is within a set range, the current parameter prediction data is used as parameter target data of the engine.
And S250, updating the prediction result of the current parameter prediction data by adopting the validity verification result of the current parameter prediction data.
In this embodiment, if the validity verification result of the parameter prediction data of this time is valid and the prediction result is invalid, the prediction result is updated to be valid; and if the validity verification result of the parameter prediction data is invalid and the prediction result is valid, updating the prediction result to be invalid.
Further, the effectiveness prediction model is updated by adopting the current parameter prediction data and the updated prediction result of the current parameter prediction data, and the historical parameter prediction data and the updated prediction result of the historical parameter prediction data.
Specifically, the present parameter prediction data and the updated prediction result of the present parameter prediction data, and the updated prediction results of the historical parameter prediction data and the historical parameter prediction data are adopted to reconstruct the training sample, and the neural network model is retrained based on the new training sample by adopting the method in the foregoing embodiment, so as to obtain the updated effectiveness prediction model.
According to the technical scheme of the embodiment of the invention, under the condition that the prediction result is that the current parameter prediction data is valid, whether the prediction result is the same as the validity verification result of the current parameter prediction data determined based on the simulation model or not is judged, and if the prediction result is the same as the validity verification result of the current parameter prediction data determined based on the simulation model, whether the current parameter prediction data is the parameter target data of the engine or not is determined according to the comparison result between the actual working scene data of the engine and the output data of the current parameter prediction data based on the simulation model; if not, executing the validity verification result of the parameter prediction data of this time, and updating the prediction result of the parameter prediction data of this time. By the technical scheme, the times of evaluating illegal parameters are reduced, and the efficiency of engine model correction is greatly improved.
EXAMPLE III
Fig. 3 is a block diagram of an engine parameter determination apparatus according to a third embodiment of the present invention; the embodiment can be applied to the situation of aircraft engine model correction. The method may be performed by an engine parameter determination device, which may be implemented in software and/or hardware, and may be integrated in an electronic device, such as a server, that carries out the engine parameter determination function.
As shown in fig. 3, the apparatus includes a data determination module 310, a validity prediction module 320, and a parameter target data determination module 330, wherein,
the data determination module 310 is configured to obtain current parameter prediction data of a simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm;
the effectiveness prediction module 320 is used for predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model;
and the parameter target data determining module 330 is configured to determine whether the current parameter prediction data is the parameter target data of the engine according to a comparison result between the actual working scene data of the engine and output data of the current parameter prediction data based on the simulation model, when the prediction result is that the current parameter prediction data is valid.
According to the technical scheme of the embodiment of the invention, the current parameter prediction data of the simulation model corresponding to the engine is obtained based on the last parameter prediction data through an intelligent optimization algorithm, then the effectiveness of the current parameter prediction data is predicted based on an effectiveness prediction model, and further whether the current parameter prediction data is the parameter target data of the engine is determined according to the comparison result between the actual working scene data of the engine and the output data of the current parameter prediction data based on the simulation model under the condition that the prediction result is that the current parameter prediction data is effective. By the technical scheme, the times of evaluating illegal parameters are reduced, and the efficiency of engine model correction is greatly improved.
Further, the parameter target data determining module 330 is specifically configured to:
and if the prediction result is the same as the validity verification result of the parameter prediction data of this time determined based on the simulation model, determining whether the parameter prediction data of this time is the parameter target data of the engine according to the comparison result between the actual working scene data and the output data of the parameter prediction data of this time based on the simulation model.
Further, the parameter target data determining module 330 comprises a parameter target data determining unit for:
and if the numerical value change quantity between the actual working scene data and the output data of the current parameter prediction data based on the simulation model is in a set range, taking the current parameter prediction data as the parameter target data of the engine.
Further, the effectiveness prediction model is determined by:
based on the parameter value range of the simulation model, performing Latin square sampling to obtain a training sample;
determining an effectiveness verification result of the training sample based on the simulation model;
and training the neural network model by adopting the training samples and the effectiveness verification results of the training samples to obtain an effectiveness prediction model.
Further, the parameter target data determining module 330 is further specifically configured to:
and if the prediction result is different from the validity verification result of the parameter prediction data determined based on the simulation model, updating the prediction result of the parameter prediction data by adopting the validity verification result of the parameter prediction data.
Further, the parameter target data determining module 330 further comprises an updating unit for:
and updating the effectiveness prediction model by adopting the current parameter prediction data and the updated prediction result of the historical parameter prediction data.
The engine parameter determining device can execute the engine parameter determining method provided by any embodiment of the application, and has corresponding functional modules and beneficial effects of the executing method.
Example four
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention, and fig. 4 shows a block diagram of an exemplary electronic device suitable for implementing the embodiment of the present invention. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in FIG. 4, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory (cache 32). The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the engine parameter determination method provided in the embodiment of the present application.
EXAMPLE five
Fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program (or referred to as computer-executable instructions) is stored, where the computer program, when executed by a processor, can be used to execute the method for determining the engine parameter provided in any of the above embodiments of the present invention, where the method includes:
obtaining the parameter prediction data of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm;
predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model;
and under the condition that the prediction result is that the parameter prediction data of this time is effective, determining whether the parameter prediction data of this time is parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the parameter prediction data of this time based on the simulation model.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A 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 of the foregoing. 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 Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the embodiments of the present invention have been described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and many other equivalent embodiments may be included without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of determining an engine parameter, comprising:
obtaining the parameter prediction data of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm;
predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model;
and under the condition that the prediction result is that the parameter prediction data of this time is effective, determining whether the parameter prediction data of this time is parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the parameter prediction data of this time based on the simulation model.
2. The method according to claim 1, wherein the determining whether the present parameter prediction data is parameter target data of the engine according to a result of comparison between actual operation scene data of the engine and output data of the present parameter prediction data based on a simulation model, comprises:
and if the prediction result is the same as the validity verification result of the parameter prediction data of this time determined based on the simulation model, determining whether the parameter prediction data of this time is the parameter target data of the engine according to the comparison result between the actual working scene data and the output data of the parameter prediction data of this time based on the simulation model.
3. The method according to claim 1, wherein the determining whether the present parameter prediction data is parameter target data of the engine according to a result of comparison between actual operation scene data of the engine and output data of the present parameter prediction data based on a simulation model, comprises:
and if the numerical value change quantity between the actual working scene data and the output data of the current parameter prediction data based on the simulation model is in a set range, taking the current parameter prediction data as the parameter target data of the engine.
4. The method of claim 1, wherein the effectiveness prediction model is determined by:
based on the parameter value range of the simulation model, performing Latin square sampling to obtain a training sample;
determining a validity verification result of the training sample based on the simulation model;
and training a neural network model by adopting the training samples and the effectiveness verification results of the training samples to obtain an effectiveness prediction model.
5. The method of claim 2, further comprising:
and if the prediction result is different from the validity verification result of the parameter prediction data of this time determined based on the simulation model, updating the prediction result of the parameter prediction data of this time by adopting the validity verification result of the parameter prediction data of this time.
6. The method of claim 5, further comprising:
updating the effectiveness prediction model by adopting the current parameter prediction data and the updated prediction result of the current parameter prediction data as well as the historical parameter prediction data and the updated prediction result of the historical parameter prediction data.
7. An engine parameter determination apparatus, comprising:
the data determination module is used for obtaining the parameter prediction data of the simulation model corresponding to the engine based on the last parameter prediction data through an intelligent optimization algorithm;
the effectiveness prediction module is used for predicting the effectiveness of the parameter prediction data based on an effectiveness prediction model;
and the parameter target data determining module is used for determining whether the current parameter prediction data is the parameter target data of the engine according to the comparison result between the actual working scene data of the engine and the output data of the current parameter prediction data based on the simulation model under the condition that the prediction result is that the current parameter prediction data is effective.
8. The apparatus of claim 7, wherein the parameter objective data determining module is specifically configured to:
and if the prediction result is the same as the validity verification result of the parameter prediction data of this time determined based on the simulation model, determining whether the parameter prediction data of this time is the parameter target data of the engine according to the comparison result between the actual working scene data and the output data of the parameter prediction data of this time based on the simulation model.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of determining an engine parameter as recited in any of claims 1-6.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of determining an engine parameter according to any one of claims 1-6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114154294A (en) * | 2021-10-20 | 2022-03-08 | 中国航发四川燃气涡轮研究院 | Virtual test framework for high-altitude simulation test of aircraft engine |
| CN114459574A (en) * | 2022-02-10 | 2022-05-10 | 电子科技大学 | Automatic high-speed fluid flow measurement accuracy rate evaluation method and device and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086545A (en) * | 2018-08-16 | 2018-12-25 | 广州锦红源电子科技有限公司 | Engine pedestal demarcates test optimization method, computer equipment and system |
| CN109598066A (en) * | 2018-12-05 | 2019-04-09 | 百度在线网络技术（北京）有限公司 | Effect evaluation method, device, equipment and the storage medium of prediction module |
| CN110119570A (en) * | 2019-05-10 | 2019-08-13 | 东北电力大学 | A kind of farm model parameters validation method of measured data driving |
| CN110516275A (en) * | 2019-05-31 | 2019-11-29 | 国网辽宁省电力有限公司电力科学研究院 | Simulation parameters check method based on disturbance information |
| US20200184131A1 (en) * | 2018-06-27 | 2020-06-11 | Dalian University Of Technology | A method for prediction of key performance parameter of an aero-engine transition state acceleration process based on space reconstruction |
| CN111339686A (en) * | 2020-03-26 | 2020-06-26 | 西北工业大学 | Turboshaft engine reverse modeling method based on test data |
| CN112507452A (en) * | 2020-11-30 | 2021-03-16 | 南京航空航天大学 | Digital twin modeling method for reliability of turbine blade of aircraft engine |
| CN112800541A (en) * | 2021-01-26 | 2021-05-14 | 南京航空航天大学 | Aero-engine load spectrum task segment modeling method based on support vector machine regression |
-
2021
- 2021-06-22 CN CN202110689579.7A patent/CN113408070B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200184131A1 (en) * | 2018-06-27 | 2020-06-11 | Dalian University Of Technology | A method for prediction of key performance parameter of an aero-engine transition state acceleration process based on space reconstruction |
| CN109086545A (en) * | 2018-08-16 | 2018-12-25 | 广州锦红源电子科技有限公司 | Engine pedestal demarcates test optimization method, computer equipment and system |
| CN109598066A (en) * | 2018-12-05 | 2019-04-09 | 百度在线网络技术（北京）有限公司 | Effect evaluation method, device, equipment and the storage medium of prediction module |
| CN110119570A (en) * | 2019-05-10 | 2019-08-13 | 东北电力大学 | A kind of farm model parameters validation method of measured data driving |
| CN110516275A (en) * | 2019-05-31 | 2019-11-29 | 国网辽宁省电力有限公司电力科学研究院 | Simulation parameters check method based on disturbance information |
| CN111339686A (en) * | 2020-03-26 | 2020-06-26 | 西北工业大学 | Turboshaft engine reverse modeling method based on test data |
| CN112507452A (en) * | 2020-11-30 | 2021-03-16 | 南京航空航天大学 | Digital twin modeling method for reliability of turbine blade of aircraft engine |
| CN112800541A (en) * | 2021-01-26 | 2021-05-14 | 南京航空航天大学 | Aero-engine load spectrum task segment modeling method based on support vector machine regression |
Non-Patent Citations (8)
| Title |
|---|
| 薛文鹏等: "基于数据的发动机模型匹配方法研究", 《航空发动机》 * |
| 薛文鹏等: "基于数据的发动机模型匹配方法研究", 《航空发动机》, vol. 43, no. 05, 31 October 2017 (2017-10-31), pages 97 - 102 * |
| 薛文鹏等: "基于整机试验数据的发动机部件特性修正方法研究", 《航空科学技术》 * |
| 薛文鹏等: "基于整机试验数据的发动机部件特性修正方法研究", 《航空科学技术》, vol. 28, no. 01, 15 January 2017 (2017-01-15), pages 54 - 58 * |
| 郭政波等: "基于数据的航空发动机模型匹配方法", 《工程与试验》 * |
| 郭政波等: "基于数据的航空发动机模型匹配方法", 《工程与试验》, vol. 60, no. 01, 15 March 2020 (2020-03-15), pages 24 - 27 * |
| 阚胜男等: "基于仿真和流量估计的服务性交通指标预测", 《同济大学学报(自然科学版)》 * |
| 阚胜男等: "基于仿真和流量估计的服务性交通指标预测", 《同济大学学报(自然科学版)》, vol. 36, no. 11, 15 November 2008 (2008-11-15), pages 1512 - 1515 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114154294A (en) * | 2021-10-20 | 2022-03-08 | 中国航发四川燃气涡轮研究院 | Virtual test framework for high-altitude simulation test of aircraft engine |
| CN114459574A (en) * | 2022-02-10 | 2022-05-10 | 电子科技大学 | Automatic high-speed fluid flow measurement accuracy rate evaluation method and device and storage medium |
| CN114459574B (en) * | 2022-02-10 | 2023-09-26 | 电子科技大学 | Automatic evaluation method and device for high-speed fluid flow measurement accuracy and storage medium |
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