CN116822058A - Diagnosis optimization method, system, equipment and storage medium based on weak sheet metal - Google Patents
Diagnosis optimization method, system, equipment and storage medium based on weak sheet metal Download PDFInfo
- Publication number
- CN116822058A CN116822058A CN202310828126.7A CN202310828126A CN116822058A CN 116822058 A CN116822058 A CN 116822058A CN 202310828126 A CN202310828126 A CN 202310828126A CN 116822058 A CN116822058 A CN 116822058A
- Authority
- CN
- China
- Prior art keywords
- sheet metal
- preset
- nvh
- test
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 137
- 239000002184 metal Substances 0.000 title claims abstract description 103
- 238000003745 diagnosis Methods 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000004088 simulation Methods 0.000 claims abstract description 91
- 238000012549 training Methods 0.000 claims abstract description 65
- 238000013461 design Methods 0.000 claims abstract description 58
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 29
- 238000012360 testing method Methods 0.000 claims description 128
- 230000004044 response Effects 0.000 claims description 23
- 230000005284 excitation Effects 0.000 claims description 18
- 238000010009 beating Methods 0.000 claims 2
- 238000004458 analytical method Methods 0.000 abstract description 30
- 238000004364 calculation method Methods 0.000 abstract description 16
- 230000008569 process Effects 0.000 abstract description 9
- 230000007246 mechanism Effects 0.000 abstract description 5
- 238000012706 support-vector machine Methods 0.000 description 41
- 238000010801 machine learning Methods 0.000 description 29
- 230000008859 change Effects 0.000 description 14
- 238000004891 communication Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000005259 measurement Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000009825 accumulation Methods 0.000 description 3
- 238000013524 data verification Methods 0.000 description 3
- 238000013400 design of experiment Methods 0.000 description 3
- 238000002405 diagnostic procedure Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a diagnosis optimization method, a system, equipment and a storage medium based on weak sheet metal, wherein the method comprises the following steps: identifying NVH problem frequency after the vehicle design is increased; the method comprises the steps of obtaining NTF/VTF curve data after vehicle design is changed and increased, inputting the NTF/VTF curve data into a preset NVH problem simulation optimization model, and obtaining a performance problem point diagnosis report of NVH problem frequency and a vehicle body sheet metal part optimization scheme, wherein the preset NVH problem simulation optimization model is trained based on an SVM algorithm; and (3) processing the weak sheet metal according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body. Compared with the modeling method and analysis flow of the mechanism in the prior art, the method and the system have the advantages that the problem that a large amount of calculation force is consumed in the real-time operation process is caused, and the NVH state analysis result of the vehicle is obtained based on the preset NVH problem simulation optimization model obtained through SVM algorithm training, so that the calculation force consumption in NVH analysis is reduced.
Description
Technical Field
The invention relates to the technical field of vehicle NVH (noise and harshness), in particular to a diagnosis optimization method, system, equipment and storage medium based on weak sheet metal.
Background
The traditional simulation of noise, vibration and Harshness (NVH) requires a lot of time and calculation resources, and the later-stage positioning problem structure and the optimization structure require resubmitting simulation calculation, which requires more experience reserves of engineers. In the latter parametric model introduction, a problem frequency database is built according to vibration transfer function (NTF)/noise transfer function (VTF) performances of a single model vehicle, meanwhile, a mode (ODS) feature database under a working state of relevant frequencies of the model vehicle is built, when the NVH optimization work is changed by facing the design of the modified model vehicle or small-amplitude design, an engineer can change structural setting parameters through empirical design, then, NTF/VTF performance simulation is carried out through changing the parameters to obtain a large number of ODS output results, a response surface method is completed according to the results, a continuous variable surface model is built, and the output results are automatically predicted after the set parameters are provided on the premise of not simulation, but the method still needs to manually select and build a response surface. Therefore, how to reduce the power consumption in NVH analysis is a urgent problem to be solved.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a diagnosis optimization method, a diagnosis optimization system, diagnosis optimization equipment and a diagnosis optimization storage medium based on weak metal plates, and aims to solve the technical problem of how to reduce calculation power consumption in NVH analysis.
In order to achieve the above object, the present invention provides a diagnosis optimization method based on weak sheet metal, which includes:
identifying NVH problem frequency after the vehicle design is increased, and acquiring NTF/VTF curve data after the vehicle design is increased;
inputting the NTF/VTF curve data into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, wherein the preset NVH problem simulation optimization model is trained based on an SVM algorithm;
and processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body.
Optionally, before the step of identifying the NVH problem frequency after the vehicle design is changed, the method includes:
collecting NTF/VTF curve data of a target project vehicle model, and associating ODS, TPA and panel contribution amounts corresponding to the target project vehicle model under different frequency excitation points and response points;
Taking NTF/VTF curve data of the target project vehicle model as input parameters, and taking corresponding ODS, TPA and panel contribution under the excitation points and the response points with different frequencies as output parameters;
and training an initial network model through an SVM algorithm based on the input parameters and the output parameters to obtain a preset NVH problem simulation optimization model.
Optionally, the step of training an initial network model through an SVM algorithm based on the input parameter and the output parameter to obtain a preset NVH problem simulation optimization model includes:
placing the input parameters and the output parameters in a target file;
reading the target file according to a target path, and dividing input parameters and output parameters in the target file into a first list and a second list respectively, wherein the input parameters and the output parameters have an association relation;
determining a training set and a testing set according to a preset dividing rule according to the input parameters in the first list and the output parameters in the second list;
training an initial network model through an SVM algorithm according to the training set to obtain a prediction set;
and determining a preset NVH problem simulation optimization model according to the prediction set and the test set.
Optionally, the step of determining a preset NVH problem simulation optimization model according to the prediction set and the test set includes:
determining the total sample size corresponding to the test set and the bit number of the parameter points;
calculating an error rate according to the prediction set, the test set, the total sample size and the bit number of the parameter points;
determining a test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set;
judging whether the error rate meets a preset error condition or not, and judging whether the test fitting degree meets a preset fitting condition or not;
and when the error rate meets the preset error condition and the test fitting degree meets the preset fitting condition, taking the trained initial network model as a preset NVH problem simulation optimization model.
Optionally, the step of determining the test fitting degree according to the total sample size, the number of bits of the parameter points, the average number of test sets, the prediction set and the test set includes:
determining the total sample size, the bit number of the parameter points and the average number of the test sets corresponding to the test sets;
and calculating the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set.
Optionally, after the step of determining whether the error rate meets a preset error condition and the test fitting degree meets a preset fitting condition, the method further includes:
and returning to the step of training an initial network model through an SVM algorithm based on the input parameter and the output parameter to obtain a preset NVH problem simulation optimization model when the error rate meets the preset error condition and the test fitting degree does not meet the preset fitting condition.
Optionally, the step of processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body includes:
determining corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased according to the performance problem point diagnosis report;
positioning weak sheet metal positions according to corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased;
and optimizing the weak sheet metal position through the optimization scheme of the sheet metal part of the vehicle body.
In addition, in order to achieve the above object, the present invention also provides a weak sheet metal based diagnosis optimization system, which includes:
The acquisition module is used for identifying NVH problem frequency after the vehicle design is increased and acquiring NTF/VTF curve data after the vehicle design is increased;
the determining module is used for inputting the NTF/VTF curve data after the vehicle design is changed into a preset NVH problem simulation optimization model so as to obtain a performance problem point diagnosis report corresponding to the NVH problem frequency and a vehicle body sheet metal part optimization scheme, and the preset NVH problem simulation optimization model is trained based on an SVM algorithm;
and the processing module is used for processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body.
In addition, in order to achieve the above object, the present invention also proposes a diagnosis optimizing apparatus based on weak sheet metal, the apparatus comprising: the system comprises a memory, a processor and a weak sheet metal based diagnostic optimizer stored on the memory and operable on the processor, the weak sheet metal based diagnostic optimizer configured to implement the steps of the weak sheet metal based diagnostic optimization method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a weak sheet metal based diagnostic optimization program which, when executed by a processor, implements the steps of the weak sheet metal based diagnostic optimization method as described above.
According to the method, firstly, NVH problem frequency after the vehicle design is changed and increased is identified, NTF/VTF curve data after the vehicle design is changed and increased is obtained, then the NTF/VTF curve data is input into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report corresponding to the NVH problem frequency and a vehicle body sheet metal part optimization scheme, the preset NVH problem simulation optimization model is trained based on an SVM algorithm, and weak sheet metal is processed according to the performance problem point diagnosis report and the vehicle body sheet metal part optimization scheme. Compared with the modeling method and analysis flow of the mechanism in the prior art, the method and the system have the advantages that the problem that a large amount of calculation force is consumed in the real-time operation process is caused, and the NVH state analysis result of the vehicle is obtained based on the preset NVH problem simulation optimization model obtained through SVM algorithm training, so that the calculation force consumption in NVH analysis is reduced.
Drawings
FIG. 1 is a schematic diagram of a diagnostic optimizing device based on weak sheet metal in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a diagnostic optimization method based on weak sheet metal according to the present invention;
FIG. 3 is a simulation flow chart of a first embodiment of the diagnostic optimization method based on weak sheet metal of the present invention;
FIG. 4 is a schematic flow chart of a second embodiment of the diagnosis optimization method based on weak sheet metal of the present invention;
fig. 5 is a block diagram of a first embodiment of the diagnostic optimizing system based on weak sheet metal of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a diagnosis optimizing device based on weak sheet metal in a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 1, the weak sheet metal-based diagnosis optimizing apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage system separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the weak sheet metal based diagnostic optimization apparatus, and may include more or fewer components than illustrated, or certain components may be combined, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a diagnosis optimizing program based on weak sheet metal may be included in the memory 1005 as one storage medium.
In the diagnosis optimizing apparatus based on weak sheet metal shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the weak sheet metal based diagnosis optimizing device of the present invention may be disposed in the weak sheet metal based diagnosis optimizing device, and the weak sheet metal based diagnosis optimizing device invokes the weak sheet metal based diagnosis optimizing program stored in the memory 1005 through the processor 1001, and executes the weak sheet metal based diagnosis optimizing method provided by the embodiment of the present invention.
The embodiment of the invention provides a diagnosis optimization method based on weak metal plates, and referring to fig. 2, fig. 2 is a flow diagram of a first embodiment of the diagnosis optimization method based on weak metal plates.
In this embodiment, the diagnosis optimization method based on weak sheet metal includes the following steps:
step S10: and identifying NVH problem frequency after the vehicle design is increased, and acquiring NTF/VTF curve data after the vehicle design is increased.
It is to be understood that the execution body of the embodiment may be a weak sheet metal-based diagnosis optimization system with functions of data processing, network communication, program running, etc., or may be other computer devices with similar functions, etc., and the embodiment is not limited.
Referring to fig. 3, fig. 3 is a simulation flow chart of a first embodiment of the diagnosis optimization method based on weak sheet metal, in which the NHV problem frequency after design change (set change) is automatically identified through a database.
The NTF/VTF curve data after the vehicle design is changed is all the NTF/VTF curve data after the vehicle design is changed.
Step S20: and inputting the NTF/VTF curve data into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, wherein the preset NVH problem simulation optimization model is trained based on an SVM algorithm.
In a specific implementation, a preset NVH problem simulation optimization model needs to be built before the NVH problem frequency after the vehicle design is increased is identified.
Further, a processing mode of constructing a preset NVH problem simulation optimization model is to collect NTF/VTF curve data of a target project vehicle model, and associate ODS, TPA and panel contribution amounts corresponding to the target project vehicle model under different frequency excitation points and response points; taking NTF/VTF curve data of a target project vehicle model as input parameters, and taking corresponding ODS, TPA and panel contribution under different frequency excitation points and response points as output parameters; and training an initial network model through an SVM algorithm based on the input parameters and the output parameters to obtain a preset NVH problem simulation optimization model.
It will be appreciated that machine learning pre-work requires a significant amount of data accumulation. The engineer determines important parts in the structure and sets parameters, wherein the parameters comprise input parameters and output parameters, the input parameters are VTF/NTF curves for acquiring corresponding project vehicle types, and the output parameters are ODS, TPA, panel contribution amounts and the like of the vehicle types under different frequency excitation and response points.
It should be further noted that the VTF/NTF curves of the corresponding project vehicle model should correlate the ODS, TPA, and panel contribution amounts of the vehicle model at different frequency excitation and response points, and then build a parameterized model according to the input parameters and the output parameters.
In the embodiment, the parameterized model avoids the time and the computing resources consumed by the simulation of a large number of traditional NVH working conditions in the later stage.
Further, training an initial network model through an SVM algorithm based on the input parameters and the output parameters, and obtaining a preset NVH problem simulation optimization model in a processing mode that the input parameters and the output parameters are placed in a target file; reading a target file according to a target path, dividing parameters in the target file into a first list and a second list, and enabling an incidence relation between input parameters and output parameters to exist; determining a training set and a testing set according to a preset dividing rule according to the input parameters in the first list and the output parameters in the second list; training an initial network model through an SVM algorithm according to the training set to obtain a prediction set; and determining a preset NVH problem simulation optimization model according to the prediction set and the test set.
It should also be understood that the preset dividing rule may be set by user, may be 7:3, may be 8:2, etc., and the embodiment is not limited thereto.
Determining the processing modes of the training set and the testing set according to the input parameters in the first list and the output parameters in the second list according to a preset dividing rule, respectively adjusting the rows and the columns of the first list and the rows and the columns of the second list so as to enable the first list and the second list to correspond to the number of parameter points, and dividing the adjusted parameters in the first list and the adjusted parameters in the second list into the training set and the testing set according to the preset dividing rule.
It should also be appreciated that the spread point generation test design method (DESIGN OF EXPERIMENT DO E) and the operating mode simulation is performed to obtain enough data points, and the input parameters and the output parameters are placed in the same excel table in xls format.
In the embodiment, based on Python language, a Scikit-learn machine learning library is called, a support vector machine algorithm is combined with simulation to perform simulation prediction, the required result is screened before output, and diagnosis and optimization schemes can be output after an NVH working condition curve (VTF/NT F) is input by combining diagnostic methods such as ODS, TPA, panel contribution and the like.
Machine learning replaces the previous response analysis method, improves the precision and further saves some time. In machine learning Python, a path is selected, and an Excel file, namely a target file, is read. The first step, dividing the data into two lists (input, i.e., a first list, and output, i.e., a second list); step two, the number of parameter points is made to correspond by adjusting the rows and columns of the two lists; and thirdly, randomly classifying the parameters into a training set and a testing set (input parameters and output parameters are simultaneously carried out), wherein the number can be modified by self, and 7:3 is recommended. And then, changing the parameter format to str (string) so that the sklearn library can calculate decimal, after the change, calling an SVM algorithm in the sklearn library, inputting a training set as an input parameter into an initial network model for training, obtaining a prediction result through training, and taking the prediction result as an output parameter.
The selected path may be a target path or a predetermined path. The Support Vector Machine (SVM) is an algorithm for performing binary classification on data according to a supervised learning mode.
Further, determining a processing mode of a preset NVH problem simulation optimization model according to the prediction set and the test set to determine the total sample size and the bit number of parameter points corresponding to the test set; calculating error rate according to the predicted set, the test set, the total sample size and the bit number of the parameter points; determining a test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set; judging whether the error rate meets a preset error condition or not, and testing whether the fitting degree meets the preset fitting condition or not; when the error rate meets the preset error condition and the test fitting degree meets the preset fitting condition, the trained initial network model is used as a preset NVH problem simulation optimization model.
It should be noted that, the preset error condition is a preset error threshold set by user definition, the error rate is required to be smaller than the preset error threshold, and when the error rate is smaller than the preset error threshold, the preset error condition is satisfied. The preset fitting conditions can be changed according to the own requirements, and the embodiment is not limited.
In a specific implementation, the prediction set (training result) is compared with the test set (real result), two measurement standards are selected to jointly evaluate the completion degree of machine learning training, one of the measurement standards is used for checking errors, the other is used for checking fitting, and machine learning is performed again under the condition that the result does not meet the requirement, so that the problem of insufficient accuracy of a plurality of machine learning algorithms is solved.
First kind: the error rate was tested.
Wherein T is the test set, P is the prediction set, n is the total sample size, i is the number of bits of the parameter points, and T [ i ] is the ith number in the test set.
Further, the processing mode for determining the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test sets is to determine the total sample size corresponding to the test sets, the bit number of the parameter points and the average number of the test sets; and calculating the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set.
Second kind: and testing the fitting degree.
Wherein a is the average of all test sets of the parameter, namely the average of the test sets, P is a prediction set, T is a test set, n is the total sample size, i is the number of bits of the parameter points, and R is the test fitting degree.
In this embodiment, if the calculation result of the R party does not meet the set condition (may be changed according to the own requirement), the operation of training the initial network model by the SVM algorithm based on the input parameter and the output parameter to obtain the preset NVH problem simulation optimization model is returned, and the machine learning data verification result is output in the form of an image and a text.
The image is the comparison of all parameter points of the true value and the predicted value of each output parameter, the characters are the specific values of the error rate and the R side of each output parameter, and when the error rate and the R side reach the training setting targets, namely, the NVH problem data of the project vehicle model are trained, the project vehicle model NVH problem data can be put into practical use.
In this embodiment, the NTF and VTF curves after the subsequent design change of the vehicle model are input, and the database completed through machine learning training is utilized to output the diagnosis report of the NVH performance problem point (the failure standard item) under the project design change model, and meanwhile, the optimization scheme of the sheet metal part of the vehicle body under the problem frequency is output, so as to complete the analysis closed loop.
After the preset NVH problem simulation optimization model is put into practical use, the NVH problem frequency after the setting is automatically identified through a database, and then NTF/VTF curve data after the vehicle design is increased is input into the preset NVH problem simulation optimization model so as to obtain a performance problem point diagnosis report corresponding to the NVH problem frequency and a vehicle body sheet metal part optimization scheme.
Step S30: and processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body.
In this embodiment, corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased are determined according to the performance problem point diagnosis report, then the weak sheet metal positions are positioned according to the corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased, and then the weak sheet metal is optimized through a vehicle body sheet metal part optimization scheme based on the weak sheet metal positions. In the simulation analysis of the NVH practical problems, the analysis flow of the working conditions of analysis items such as ODS, TPA, panel contribution and the like which are highly dependent on the experience of engineers and need to be subjected to multiple checking is redefined, and the embodiment realizes single-input (NTF/VTF) multi-working-condition output (problem frequency, panel and optimization suggestion) through machine learning training, so that the simulation efficiency is improved.
In the embodiment, firstly, the NVH problem frequency after the vehicle design is increased is identified, NTF/VTF curve data after the vehicle design is increased is obtained, then the NTF/VTF curve data is input into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, the preset NVH problem simulation optimization model is trained based on an SVM algorithm, and then weak sheet metal is processed according to the performance problem point diagnosis report and the vehicle body sheet metal part optimization scheme. Compared with the modeling method and analysis flow of the mechanism in the prior art, the method and the system have the advantages that the problem that a large amount of calculation force is consumed in the real-time operation process is caused, and the NVH state analysis result of the vehicle is obtained based on the preset NVH problem simulation optimization model obtained through SVM algorithm training, so that the calculation force consumption in NVH analysis is reduced.
Referring to fig. 4, fig. 4 is a schematic flow chart of a second embodiment of the diagnosis optimizing method based on weak sheet metal according to the present invention.
Based on the first embodiment, in this embodiment, before step S10, the method further includes:
step S01: and acquiring NTF/VTF curve data of a target project vehicle model, and associating ODS, TPA and panel contribution amounts corresponding to the target project vehicle model at different frequency excitation points and response points.
Step S02: and taking the NTF/VTF curve data of the target project vehicle model as an input parameter, and taking the corresponding ODS, TPA and panel contribution under the excitation points and the response points with different frequencies as output parameters.
It will be appreciated that machine learning pre-work requires a significant amount of data accumulation. The engineer determines important parts in the structure and sets parameters, wherein the parameters comprise input parameters and output parameters, the input parameters are VTF/NTF curves for acquiring corresponding project vehicle types, and the output parameters are ODS, TPA, panel contribution amounts and the like of the vehicle types under different frequency excitation and response points.
It should be further noted that the VTF/NTF curves of the corresponding project vehicle model should correlate the ODS, TPA, and panel contribution amounts of the vehicle model at different frequency excitation and response points, and then build a parameterized model according to the input parameters and the output parameters.
In the embodiment, the parameterized model avoids the time and the computing resources consumed by the simulation of a large number of traditional NVH working conditions in the later stage.
Step S03: and training an initial network model through an SVM algorithm based on the input parameters and the output parameters to obtain a preset NVH problem simulation optimization model.
Further, training an initial network model through an SVM algorithm based on the input parameters and the output parameters, and obtaining a preset NVH problem simulation optimization model in a processing mode that the input parameters and the output parameters are placed in a target file; reading a target file according to the target path, and dividing parameters in the target file into a first list and a second list; respectively adjusting the rows and columns of the first list and the rows and columns of the second list so as to enable the first list and the second list to correspond to the number of parameter points; dividing parameters in the adjusted first list and the adjusted second list into a training set and a testing set; training an initial network model through an SVM algorithm according to the training set to obtain a prediction set; and determining a preset NVH problem simulation optimization model according to the prediction set and the test set.
It should also be appreciated that the spread point generation test design method (DESIGN OF EXPERIMENT DO E) and the operating mode simulation is performed to obtain enough data points, and the input parameters and the output parameters are placed in the same excel table in xls format.
In the embodiment, based on Python language, a Scikit-learn machine learning library is called, a support vector machine algorithm is combined with simulation to perform simulation prediction, the required result is screened before output, and diagnosis and optimization schemes can be output after an NVH working condition curve (VTF/NT F) is input by combining diagnostic methods such as ODS, TPA, panel contribution and the like.
Machine learning replaces the previous response analysis method, improves the precision and further saves some time. In machine learning Python, a path is selected, and an Excel file, namely a target file, is read. The first step, dividing the data into two lists (input, i.e., a first list, and output, i.e., a second list); step two, the number of parameter points is made to correspond by adjusting the rows and columns of the two lists; and thirdly, randomly classifying the parameters into a training set and a testing set (input parameters and output parameters are simultaneously carried out), wherein the number can be modified by self, and 7:3 is recommended. And then, changing the parameter format to str (string) so that the sklearn library can calculate decimal, after the change, calling an SVM algorithm in the sklearn library, inputting a training set as an input parameter into an initial network model for training, obtaining a prediction result through training, and taking the prediction result as an output parameter.
The selected path may be a target path or a predetermined path. The Support Vector Machine (SVM) is an algorithm for performing binary classification on data according to a supervised learning mode.
Further, determining a processing mode of a preset NVH problem simulation optimization model according to the prediction set and the test set to determine the total sample size and the bit number of parameter points corresponding to the test set; calculating error rate according to the predicted set, the test set, the total sample size and the bit number of the parameter points; determining a test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set; judging whether the error rate meets a preset error condition or not, and testing whether the fitting degree meets the preset fitting condition or not; when the error rate meets the preset error condition and the test fitting degree meets the preset fitting condition, the trained initial network model is used as a preset NVH problem simulation optimization model.
It should be noted that, the preset error condition is a preset error threshold set by user definition, the error rate is required to be smaller than the preset error threshold, and when the error rate is smaller than the preset error threshold, the preset error condition is satisfied. The preset fitting conditions can be changed according to the own requirements, and the embodiment is not limited.
In a specific implementation, the prediction set (training result) is compared with the test set (real result), two measurement standards are selected to jointly evaluate the completion degree of machine learning training, one of the measurement standards is used for checking errors, the other is used for checking fitting, and machine learning is performed again under the condition that the result does not meet the requirement, so that the problem of insufficient accuracy of a plurality of machine learning algorithms is solved.
First kind: the error rate was tested.
Wherein T is the test set, P is the prediction set, n is the total sample size, i is the number of bits of the parameter points, and T [ i ] is the ith number in the test set.
Further, the processing mode for determining the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test sets is to determine the total sample size corresponding to the test sets, the bit number of the parameter points and the average number of the test sets; and calculating the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set.
Second kind: and testing the fitting degree.
Wherein a is the average of all test sets of the parameter, namely the average of the test sets, P is a prediction set, T is a test set, n is the total sample size, i is the number of bits of the parameter points, and R is the test fitting degree.
In this embodiment, if the calculation result of the R party does not meet the set condition (may be changed according to the own requirement), the operation of training the initial network model by the SVM algorithm based on the input parameter and the output parameter to obtain the preset NVH problem simulation optimization model is returned, and the machine learning data verification result is output in the form of an image and a text.
The image is the comparison of all parameter points of the true value and the predicted value of each output parameter, the characters are the specific values of the error rate and the R side of each output parameter, and when the error rate and the R side reach the training setting targets, namely, the NVH problem data of the project vehicle model are trained, the project vehicle model NVH problem data can be put into practical use.
In this embodiment, the NTF and VTF curves after the subsequent design change of the vehicle model are input, and the database completed through machine learning training is utilized to output the diagnosis report of the NVH performance problem point (the failure standard item) under the project design change model, and meanwhile, the optimization scheme of the sheet metal part of the vehicle body under the problem frequency is output, so as to complete the analysis closed loop.
In the embodiment, firstly, NTF/VTF curve data of a target project vehicle model are collected, ODS, TPA and panel contribution amounts corresponding to the target project vehicle model under different frequency excitation points and response points are associated, then the NTF/VTF curve data of the target project vehicle model is used as input parameters, the ODS, TPA and panel contribution amounts corresponding to the different frequency excitation points and response points are used as output parameters, then an initial network model is trained through an SVM algorithm based on the input parameters and the output parameters, a preset NVH problem simulation optimization model is obtained, a set NTF/VTF curve is input when a project design of a certain vehicle model is changed, and a simulation analysis optimization closed loop of a problem frequency sheet metal diagnosis and optimization scheme is output, so that simulation efficiency is improved.
Referring to fig. 5, fig. 5 is a block diagram of a first embodiment of the weak sheet metal based diagnostic optimization system of the present invention.
As shown in fig. 5, the diagnosis optimization system based on weak sheet metal provided by the embodiment of the invention includes:
the acquisition module 5001 is configured to identify an increased NVH problem frequency of the vehicle design, and acquire NTF/VTF curve data of the vehicle design after the increase.
It is to be understood that the execution body of the embodiment may be a weak sheet metal-based diagnosis optimization system with functions of data processing, network communication, program running, etc., or may be other computer devices with similar functions, etc., and the embodiment is not limited.
Referring to fig. 3, fig. 3 is a simulation flow chart of a first embodiment of the diagnosis optimization method based on weak sheet metal, in which the NHV problem frequency after design change (set change) is automatically identified through a database.
The NTF/VTF curve data after the vehicle design is changed is all the NTF/VTF curve data after the vehicle design is changed.
The determining module 5002 is configured to input the NTF/VTF curve data after the vehicle design is increased to a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, where the preset NVH problem simulation optimization model is trained based on an SVM algorithm.
In a specific implementation, a preset NVH problem simulation optimization model needs to be built before the NVH problem frequency after the vehicle design is increased is identified.
Further, a processing mode of constructing a preset NVH problem simulation optimization model is to collect NTF/VTF curve data of a target project vehicle model, and associate ODS, TPA and panel contribution amounts corresponding to the target project vehicle model under different frequency excitation points and response points; taking NTF/VTF curve data of a target project vehicle model as input parameters, and taking corresponding ODS, TPA and panel contribution under different frequency excitation points and response points as output parameters; and training an initial network model through an SVM algorithm based on the input parameters and the output parameters to obtain a preset NVH problem simulation optimization model.
It will be appreciated that machine learning pre-work requires a significant amount of data accumulation. The engineer determines important parts in the structure and sets parameters, wherein the parameters comprise input parameters and output parameters, the input parameters are VTF/NTF curves for acquiring corresponding project vehicle types, and the output parameters are ODS, TPA, panel contribution amounts and the like of the vehicle types under different frequency excitation and response points.
It should be further noted that the VTF/NTF curves of the corresponding project vehicle model should correlate the ODS, TPA, and panel contribution amounts of the vehicle model at different frequency excitation and response points, and then build a parameterized model according to the input parameters and the output parameters.
In the embodiment, the parameterized model avoids the time and the computing resources consumed by the simulation of a large number of traditional NVH working conditions in the later stage.
Further, training an initial network model through an SVM algorithm based on the input parameters and the output parameters, and obtaining a preset NVH problem simulation optimization model in a processing mode that the input parameters and the output parameters are placed in a target file; reading a target file according to a target path, dividing parameters in the target file into a first list and a second list, and enabling an incidence relation between input parameters and output parameters to exist; determining a training set and a testing set according to a preset dividing rule according to the input parameters in the first list and the output parameters in the second list; training an initial network model through an SVM algorithm according to the training set to obtain a prediction set; and determining a preset NVH problem simulation optimization model according to the prediction set and the test set.
It should also be understood that the preset dividing rule may be set by user, may be 7:3, may be 8:2, etc., and the embodiment is not limited thereto.
Determining the processing modes of the training set and the testing set according to the input parameters in the first list and the output parameters in the second list according to a preset dividing rule, respectively adjusting the rows and the columns of the first list and the rows and the columns of the second list so as to enable the first list and the second list to correspond to the number of parameter points, and dividing the adjusted parameters in the first list and the adjusted parameters in the second list into the training set and the testing set according to the preset dividing rule.
It should also be appreciated that the spread point generation test design method (DESIGN OF EXPERIMENT DO E) and the operating mode simulation is performed to obtain enough data points, and the input parameters and the output parameters are placed in the same excel table in xls format.
In the embodiment, based on Python language, a Scikit-learn machine learning library is called, a support vector machine algorithm is combined with simulation to perform simulation prediction, the required result is screened before output, and diagnosis and optimization schemes can be output after an NVH working condition curve (VTF/NT F) is input by combining diagnostic methods such as ODS, TPA, panel contribution and the like.
Machine learning replaces the previous response analysis method, improves the precision and further saves some time. In machine learning Python, a path is selected, and an Excel file, namely a target file, is read. The first step, dividing the data into two lists (input, i.e., a first list, and output, i.e., a second list); step two, the number of parameter points is made to correspond by adjusting the rows and columns of the two lists; and thirdly, randomly classifying the parameters into a training set and a testing set (input parameters and output parameters are simultaneously carried out), wherein the number can be modified by self, and 7:3 is recommended. And then, changing the parameter format to str (string) so that the sklearn library can calculate decimal, after the change, calling an SVM algorithm in the sklearn library, inputting a training set as an input parameter into an initial network model for training, obtaining a prediction result through training, and taking the prediction result as an output parameter.
The selected path may be a target path or a predetermined path. The Support Vector Machine (SVM) is an algorithm for performing binary classification on data according to a supervised learning mode.
Further, determining a processing mode of a preset NVH problem simulation optimization model according to the prediction set and the test set to determine the total sample size and the bit number of parameter points corresponding to the test set; calculating error rate according to the predicted set, the test set, the total sample size and the bit number of the parameter points; determining a test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set; judging whether the error rate meets a preset error condition or not, and testing whether the fitting degree meets the preset fitting condition or not; when the error rate meets the preset error condition and the test fitting degree meets the preset fitting condition, the trained initial network model is used as a preset NVH problem simulation optimization model.
It should be noted that, the preset error condition is a preset error threshold set by user definition, the error rate is required to be smaller than the preset error threshold, and when the error rate is smaller than the preset error threshold, the preset error condition is satisfied. The preset fitting conditions can be changed according to the own requirements, and the embodiment is not limited.
In a specific implementation, the prediction set (training result) is compared with the test set (real result), two measurement standards are selected to jointly evaluate the completion degree of machine learning training, one of the measurement standards is used for checking errors, the other is used for checking fitting, and machine learning is performed again under the condition that the result does not meet the requirement, so that the problem of insufficient accuracy of a plurality of machine learning algorithms is solved.
First kind: the error rate was tested.
Wherein T is the test set, P is the prediction set, n is the total sample size, i is the number of bits of the parameter points, and T [ i ] is the ith number in the test set.
Further, the processing mode for determining the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test sets is to determine the total sample size corresponding to the test sets, the bit number of the parameter points and the average number of the test sets; and calculating the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set.
Second kind: and testing the fitting degree.
Wherein a is the average of all test sets of the parameter, namely the average of the test sets, P is a prediction set, T is a test set, n is the total sample size, i is the number of bits of the parameter points, and R is the test fitting degree.
In this embodiment, if the calculation result of the R party does not meet the set condition (may be changed according to the own requirement), the operation of training the initial network model by the SVM algorithm based on the input parameter and the output parameter to obtain the preset NVH problem simulation optimization model is returned, and the machine learning data verification result is output in the form of an image and a text.
The image is the comparison of all parameter points of the true value and the predicted value of each output parameter, the characters are the specific values of the error rate and the R side of each output parameter, and when the error rate and the R side reach the training setting targets, namely, the NVH problem data of the project vehicle model are trained, the project vehicle model NVH problem data can be put into practical use.
In this embodiment, the NTF and VTF curves after the subsequent design change of the vehicle model are input, and the database completed through machine learning training is utilized to output the diagnosis report of the NVH performance problem point (the failure standard item) under the project design change model, and meanwhile, the optimization scheme of the sheet metal part of the vehicle body under the problem frequency is output, so as to complete the analysis closed loop.
After the preset NVH problem simulation optimization model is put into practical use, the NVH problem frequency after the setting is automatically identified through a database, and then NTF/VTF curve data after the vehicle design is increased is input into the preset NVH problem simulation optimization model so as to obtain a performance problem point diagnosis report corresponding to the NVH problem frequency and a vehicle body sheet metal part optimization scheme.
And the processing module 5003 is used for processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body.
In this embodiment, corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased are determined according to the performance problem point diagnosis report, then the weak sheet metal positions are positioned according to the corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased, and then the weak sheet metal is optimized through a vehicle body sheet metal part optimization scheme based on the weak sheet metal positions. In the simulation analysis of the NVH practical problems, the analysis flow of the working conditions of analysis items such as ODS, TPA, panel contribution and the like which are highly dependent on the experience of engineers and need to be subjected to multiple checking is redefined, and the embodiment realizes single-input (NTF/VTF) multi-working-condition output (problem frequency, panel and optimization suggestion) through machine learning training, so that the simulation efficiency is improved.
In the embodiment, firstly, the NVH problem frequency after the vehicle design is increased is identified, NTF/VTF curve data after the vehicle design is increased is obtained, then the NTF/VTF curve data is input into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, the preset NVH problem simulation optimization model is trained based on an SVM algorithm, and then weak sheet metal is processed according to the performance problem point diagnosis report and the vehicle body sheet metal part optimization scheme. Compared with the modeling method and analysis flow of the mechanism in the prior art, the method and the system have the advantages that the problem that a large amount of calculation force is consumed in the real-time operation process is caused, and the NVH state analysis result of the vehicle is obtained based on the preset NVH problem simulation optimization model obtained through SVM algorithm training, so that the calculation force consumption in NVH analysis is reduced.
In the embodiment, firstly, the NVH problem frequency after the vehicle design is increased is identified, then NTF/VTF curve data after the vehicle design is increased is input into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, the preset NVH problem simulation optimization model is trained based on an SVM algorithm, and then weak sheet metal is processed according to the performance problem point diagnosis report and the vehicle body sheet metal part optimization scheme. Compared with the modeling method and analysis flow of the mechanism in the prior art, the method and the system have the advantages that the problem that a large amount of calculation force is consumed in the real-time operation process is caused, and the NVH state analysis result of the vehicle is obtained based on the preset NVH problem simulation optimization model obtained through SVM algorithm training, so that the calculation force consumption in NVH analysis is reduced.
Other embodiments or specific implementation manners of the diagnosis optimization system based on weak sheet metal can refer to the above method embodiments, and are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The diagnosis optimization method based on the weak sheet metal is characterized by comprising the following steps of:
identifying NVH problem frequency after the vehicle design is increased, and acquiring NTF/VTF curve data after the vehicle design is increased;
inputting the NTF/VTF curve data into a preset NVH problem simulation optimization model to obtain a performance problem point diagnosis report and a vehicle body sheet metal part optimization scheme corresponding to the NVH problem frequency, wherein the preset NVH problem simulation optimization model is trained based on an SVM algorithm;
and processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body.
2. The method of claim 1, wherein prior to the step of identifying the increased frequency of NVH problems with the vehicle design, comprising:
collecting NTF/VTF curve data of a target project vehicle model, and associating ODS, TPA and panel contribution amounts corresponding to the target project vehicle model under different frequency excitation points and response points;
taking NTF/VTF curve data of the target project vehicle model as input parameters, and taking corresponding ODS, TPA and panel contribution under the excitation points and the response points with different frequencies as output parameters;
And training an initial network model through an SVM algorithm based on the input parameters and the output parameters to obtain a preset NVH problem simulation optimization model.
3. The method of claim 2, wherein the step of training an initial network model by an SVM algorithm based on the input parameters and the output parameters to obtain a preset NVH problem simulation optimization model comprises:
placing the input parameters and the output parameters in a target file;
reading the target file according to a target path, and dividing input parameters and output parameters in the target file into a first list and a second list respectively, wherein the input parameters and the output parameters have an association relation;
determining a training set and a testing set according to a preset dividing rule according to the input parameters in the first list and the output parameters in the second list;
training an initial network model through an SVM algorithm according to the training set to obtain a prediction set;
and determining a preset NVH problem simulation optimization model according to the prediction set and the test set.
4. The method of claim 3, wherein the step of determining a pre-set NVH problem simulation optimization model from the prediction set and the test set comprises:
Determining the total sample size corresponding to the test set and the bit number of the parameter points;
calculating an error rate according to the prediction set, the test set, the total sample size and the bit number of the parameter points;
determining a test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set;
judging whether the error rate meets a preset error condition or not, and judging whether the test fitting degree meets a preset fitting condition or not;
and when the error rate meets the preset error condition and the test fitting degree meets the preset fitting condition, taking the trained initial network model as a preset NVH problem simulation optimization model.
5. The method of claim 4, wherein the step of determining a test fit from the total sample size, the number of parameter points, a test set average, the prediction set, and the test set comprises:
determining the total sample size, the bit number of the parameter points and the average number of the test sets corresponding to the test sets;
and calculating the test fitting degree according to the total sample size, the bit number of the parameter points, the average number of the test sets, the prediction set and the test set.
6. The method of claim 4, wherein after the step of determining whether the error rate satisfies a predetermined error condition and the test fitness satisfies a predetermined fit condition, further comprising:
and returning to the step of training an initial network model through an SVM algorithm based on the input parameter and the output parameter to obtain a preset NVH problem simulation optimization model when the error rate meets the preset error condition and the test fitting degree does not meet the preset fitting condition.
7. The method of any one of claims 1-6, wherein the step of processing the weak sheet metal location based on the performance issue point diagnostic report and the body sheet metal optimization scheme comprises:
determining corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased according to the performance problem point diagnosis report;
positioning weak sheet metal positions according to corresponding ODS, TPA and node contribution amounts under the NVH problem frequency after the vehicle design is increased;
and optimizing the weak sheet metal position through the optimization scheme of the sheet metal part of the vehicle body.
8. Diagnosis optimizing system based on weak panel beating, characterized by, diagnosis optimizing system based on weak panel beating includes:
The acquisition module is used for identifying NVH problem frequency after the vehicle design is increased and acquiring NTF/VTF curve data after the vehicle design is increased;
the determining module is used for inputting the NTF/VTF curve data after the vehicle design is changed into a preset NVH problem simulation optimization model so as to obtain a performance problem point diagnosis report corresponding to the NVH problem frequency and a vehicle body sheet metal part optimization scheme, and the preset NVH problem simulation optimization model is trained based on an SVM algorithm;
and the processing module is used for processing the weak sheet metal position according to the performance problem point diagnosis report and the optimization scheme of the sheet metal part of the vehicle body.
9. A diagnostic optimizing device based on weak sheet metal, characterized in that it comprises: a memory, a processor and a sheet metal weakness-based diagnostic optimizer stored on the memory and operable on the processor, the sheet metal weakness-based diagnostic optimizer configured to implement the steps of the sheet metal weakness-based diagnostic optimization method of any one of claims 1 to 7.
10. A storage medium, wherein a weak sheet metal based diagnostic optimization program is stored on the storage medium, which when executed by a processor, implements the steps of the weak sheet metal based diagnostic optimization method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310828126.7A CN116822058A (en) | 2023-07-06 | 2023-07-06 | Diagnosis optimization method, system, equipment and storage medium based on weak sheet metal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310828126.7A CN116822058A (en) | 2023-07-06 | 2023-07-06 | Diagnosis optimization method, system, equipment and storage medium based on weak sheet metal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116822058A true CN116822058A (en) | 2023-09-29 |
Family
ID=88140724
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310828126.7A Pending CN116822058A (en) | 2023-07-06 | 2023-07-06 | Diagnosis optimization method, system, equipment and storage medium based on weak sheet metal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116822058A (en) |
-
2023
- 2023-07-06 CN CN202310828126.7A patent/CN116822058A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20070061144A1 (en) | Batch statistics process model method and system | |
| US20200372342A1 (en) | Systems and methods for predictive early stopping in neural network training | |
| EP4075281A1 (en) | Ann-based program test method and test system, and application | |
| CN107360026B (en) | Distributed message middleware performance prediction and modeling method | |
| CN110647995A (en) | Rule training method, device, equipment and storage medium | |
| CN118070609A (en) | Simulation prediction method, simulation prediction system, simulation prediction device and computer readable storage medium based on finite element parametric modeling and point cloud deep learning | |
| CN113268335B (en) | Model training and execution duration estimation method, device, equipment and storage medium | |
| CN111768247A (en) | Order-placing rate prediction method, device and readable storage medium | |
| CN114817034A (en) | Test quality analysis method and device and electronic equipment | |
| CN114443483A (en) | Test method and device of artificial intelligence system, electronic equipment and medium | |
| CN116992602B (en) | Reliability fuzzy evaluation method based on failure state characterization, electronic equipment and storage medium | |
| CN116822058A (en) | Diagnosis optimization method, system, equipment and storage medium based on weak sheet metal | |
| CN113393023B (en) | Mold quality evaluation method, apparatus, device and storage medium | |
| CN114595630A (en) | Activity effect evaluation model training method and device, computer equipment and medium | |
| US11609936B2 (en) | Graph data processing method, device, and computer program product | |
| CN113010687B (en) | Exercise label prediction method and device, storage medium and computer equipment | |
| CN115203556A (en) | Score prediction model training method and device, electronic equipment and storage medium | |
| CN114925895A (en) | Maintenance equipment prediction method, terminal and storage medium | |
| CN111340276A (en) | Method and system for generating prediction data | |
| CN111967774A (en) | Software quality risk prediction method and device | |
| CN113449382B (en) | Simulation method and system for whole vehicle emission result | |
| CN116843203B (en) | Service access processing method, device, equipment, medium and product | |
| CN116049733A (en) | Neural network-based performance evaluation method, system, equipment and storage medium | |
| CN117035515A (en) | Workload evaluation method, workload evaluation device, computer equipment and storage medium | |
| CN118194423A (en) | Vehicle body performance optimization method, system and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |